# https://www.kalvad.com/about/ ### Our Story Kalvad was founded in 2019 by two engineers who believed that software should be built to last. Pierre Guillemot and Loïc Tosser had worked across Europe, Asia, and the Middle East, and they saw the same pattern repeatedly: systems delivered quickly but designed poorly, creating technical debt that compounded over years. They started Kalvad in Dubai with a clear focus: build systems that remain reliable, maintainable, and understandable long after the initial delivery. The first project was a public-sector platform in the UAE, where security, compliance, and operational continuity were non-negotiable. That project set the standard for everything that followed. The company grew naturally, through referrals and repeat engagements with organizations that valued engineering quality. Today, Kalvad has a 30-person team in Dubai, serves over 100 clients internationally, and actively contributes to the open-source projects we rely on. Our work spans from early technical design to long-term operation, and we remain hands-on engineers first. ### Engineering Principles Engineering before marketingWe prioritize technical substance over hype. Every decision is justified by production requirements, not trends.Production over prototypesWe build systems meant to operate reliably for years, not demos that work once. Maintainability is non-negotiable.Simplicity over unnecessary complexityWe favor clarity and proven technologies. Complexity is added only when it solves a real problem.Open source mindsetWe contribute to and maintain the projects we rely on. Transparency and community drive our engineering culture.Long-term partnershipsWe build platforms meant to evolve. Our accountability extends well beyond delivery.Rigor and transparencyWe document decisions, test assumptions, and design for failure before it happens. Accountability is built into how we work. ### KALVAD IN NUMBERS 2019Founded100+Government Projects10+Conference Talks10,000+LinkedIn Followers ### Kalvad's Founders Pierre Guillemot - CEO Pierre is the co-founder and CEO of Kalvad. He leads partnerships, training initiatives, product development, and Kalvad's participation in industry events and conferences. With academic training in software engineering across Europe and Asia, including degrees from Ireland, France, and China, Pierre began his career as a software engineer at the Chinese Academy of Sciences in Beijing. He later worked across Europe before relocating to Dubai, where he co-founded Kalvad in 2019. With over a decade of experience, Pierre has a strong background in scalable system architectures, data platforms, and IoT systems. His work focuses on building long-term technology partnerships and ensuring that Kalvad's products and services remain grounded in real-world engineering constraints. Pierre believes that sustainable engineering comes from understanding the full lifecycle of a system, not just the parts that are visible at launch.Loïc Tosser - CTO Loïc is the co-founder and CTO of Kalvad, leading engineering strategy, research and development, and long-term technical architecture across client projects and internal platforms. He is a senior system engineer with deep expertise in infrastructure, security, automation, and data streaming systems. After graduating from one of Europe's leading computer science institutions, Loïc began his international career through an exchange program at a major telecommunications university in Russia, before relocating to the United Arab Emirates. Prior to and alongside Kalvad, Loïc has held CTO roles for startups and public-sector organizations in the UAE. His work focuses on designing and operating large-scale, high-availability systems, with a strong emphasis on reliability, security, and operational efficiency. Loïc approaches engineering as a discipline where simplicity and rigor compound over time, and where the best systems are those you can trust without thinking about them. ### From Foundation to Today 2019 #### Foundation Pierre Guillemot and Loïc Tosser founded Kalvad in Dubai with a focus on production-grade engineering for public and private organizations.2020 #### First Public-Sector Platform Delivered a production platform for a UAE government entity, handling critical services with strict security, availability, and compliance requirements.2021 #### University Partnership & Internship Program Partnership with the Canadian University of Dubai and launch of a structured summer internship program to develop local engineering talent.2022 #### International Expansion First international contracts delivered in France and Canada, extending Kalvad's engineering approach to European and North American clients.2023 #### Open-Source Contributions Scale Kalvad's open-source contributions grow significantly, with active maintenance of multiple projects and upstream contributions to Django, PyInfra, and Dramatiq.2024 #### AI & Data Systems in Production Delivered major AI and data platforms for public and private sector organizations, including RAG systems, analytics pipelines, and local LLM integrations.2025 #### Products & Engineering Guides Launched Hakeem and Jinn as flagship products. Published engineering guides on RAG architecture and on-premise production systems.2026 #### Conference Talks & Community Delivered talks at international conferences including FOSDEM. Served over 100 clients with a 30-person engineering team in Dubai. ### Engineering Beyond Client Projects Our engineering work extends beyond commercial projects. We contribute to open source, publish guides, speak at conferences, and partner with universities to develop the next generation of engineers.Open SourceWe maintain and contribute to production-grade open-source projects, from Django patches to infrastructure tools.Engineering GuidesWe publish practical guides on RAG architecture, on-premise production, and other topics we encounter in real projects.Conference TalksWe share our experience at international conferences, including FOSDEM and regional engineering events.University PartnershipsWe partner with the Canadian University of Dubai and run structured internship programs to develop local talent. ### Community Our work happens in Dubai, but our community extends globally. From university partnerships to conference talks, we engage with engineers and organizations that share our commitment to quality. ### Explore Kalvad ServicesData analytics & AI, cybersecurity, custom software, infrastructure, automation, and cloud services.GuidesPractical engineering guides on RAG architecture, on-premise production, and more.ContributionsOpen-source projects we maintain and the communities we support.ProductsJinn, Hakeem, and Hadir: production tools built by Kalvad.Why KalvadOur engineering approach, key projects, and what makes us different.ContactGet in touch to discuss your next project. ### Ready to build reliable systems? Let's discuss how we can help you build systems that last.Get in touch #### --- # https://www.kalvad.com/careers/ ### Why Join Kalvad Engineering-First CultureTechnical decisions are made by engineers, not managers. We prioritize code quality, maintainability and long-term thinking over quick fixes.Real Production SystemsYou'll work on systems that handle millions of requests, process terabytes of data and serve critical government and enterprise operations.Continuous LearningWe maintain a large technical library, encourage conference participation, support open-source contributions and promote internal knowledge sharing.Open SourceWe maintain and contribute to production-grade open-source projects. Your work may benefit thousands of developers worldwide.Modern TechnologiesWe work with Python, Django, PostgreSQL, and modern infrastructure tools. We adopt technologies that solve real problems, not trends.Long-Term GrowthMany of our engineers started as interns. We invest in your development and provide clear paths for technical leadership. ### Kalvad in Numbers 2019Founded30+Engineers100+Government ProjectsActiveInternship Program ### What You'll Work On Government PlatformsBuild secure, compliant platforms serving millions of citizens. Work with strict security requirements, high availability and complex data flows.AI SystemsDesign and deploy RAG architectures, LLM integrations and machine learning pipelines. Work on production AI that solves real business problems.Cloud InfrastructureArchitect and operate cloud environments across multiple providers. Work with Kubernetes, containerization and infrastructure as code.AutomationBuild CI/CD pipelines, deployment automation and infrastructure tooling. Eliminate manual work and improve engineering velocity.CybersecurityImplement security hardening, vulnerability management and compliance frameworks. Protect critical systems and sensitive data.Custom SoftwareDevelop web applications, APIs and business platforms. Work across the full stack with modern frameworks and proven methodologies. ### Internship Program Summer Internship ProgramIn partnership with Canadian University Dubai, we launched our Summer Internship Program to bridge academic excellence with real-world engineering experience.Our interns work on real production systems, not side projects or mock assignments. They receive mentorship from experienced engineers and are expected to contribute meaningfully to ongoing projects.Over the years, we have hired most of the interns who completed the program successfully. We believe internships should be a pathway to long-term growth, not a temporary checkbox. ### How We Hire 01Application ReviewAll applications are reviewed internally by our engineering team. We do not outsource resume screening.02Technical DiscussionShortlisted candidates have a technical conversation with our engineers to discuss experience, approach and expectations.03Background VerificationAll employees undergo standard background verification before onboarding. Security clearance is required for many client engagements.04Offer & OnboardingSuccessful candidates receive a direct offer from Kalvad. We provide clear expectations and support during the transition. ### Important Information Applications are accepted only through official Kalvad job postings on our website and LinkedIn page.We never work with external recruiters or recruitment agencies. Unsolicited recruiter submissions are not considered.We do not accept applications sent directly to personal email addresses or phone numbers.Some fraudulent websites repost or invent Kalvad job advertisements. Always verify openings through official Kalvad channels.Successful candidates must complete police clearance and government security clearance where required for client engagements. ### Frequently Asked Questions Do you hire internationally?+We are based in Dubai and hire for on-site roles. All positions require working from our Dubai office.Do you offer internships?+Yes. We run a Summer Internship Program in partnership with Canadian University Dubai. Interns work on real production systems and many are hired full-time after completion.Can recruiters submit candidates?+No. We manage all recruitment internally and do not partner with external recruiters or agencies. Unsolicited recruiter submissions are not considered.What's the best way to get in touch?+Apply through our official LinkedIn page for job openings, or use the website contact form for internship inquiries. We review all applications through these official channels.Why is security clearance required?+Many of our engagements involve government and enterprise clients with strict compliance requirements. Security clearance ensures we meet these obligations and protect sensitive systems. ### Ready to Join? For full-time roles, apply through our LinkedIn page. For internship inquiries, contact us through the website.View Open PositionsContact Us #### --- # https://www.kalvad.com/contact/ ### What We Can Help With Custom SoftwareWeb applications, APIs and business platforms engineered for maintainability.InfrastructureLinux, networking and production operations.CloudPublic, private and hybrid cloud environments designed around operational requirements.Data & AIAnalytics platforms, machine learning pipelines and LLM integrations.AutomationCI/CD, Infrastructure as Code and deployment automation.CybersecuritySecurity hardening, vulnerability management and compliance frameworks. ### Send Us a Message ### What Happens Next 1Enquiry receivedWe receive your message and log it internally.↓→2Engineer reviewAn engineer reviews your request to understand the scope.↓→3Introductory discussionWe schedule a call to learn more about your project.↓→4Next stepsWe recommend the most suitable approach moving forward. ### Our Office Emailcontact@kalvad.comPhone04 882 8946AddressOffice 808, Armada 2, JLT, DubaiHoursMon to Fri, 9am to 6pmLinkedInkalvad ### Frequently Asked Questions What happens after I submit the form?+Your message is received by our team and reviewed by an engineer. We will respond within one business day to acknowledge your enquiry and discuss next steps.How quickly do you respond?+We typically respond within one business day. For urgent matters, you can reach us by phone during business hours.Do you work internationally?+Yes. We are based in Dubai but work with clients worldwide. Our team has experience delivering projects across the Middle East, Europe and Asia.Can we schedule an introductory meeting?+Yes. After reviewing your enquiry, we will schedule an introductory call if there is a mutual fit. There is no obligation. The goal is to understand your objectives and evaluate whether we can help.Do you sign NDAs?+Yes. We are comfortable signing non-disclosure agreements before discussing project details. This is standard practice for many of our engagements.Can you work with our existing development team?+Yes. We regularly collaborate with in-house teams, providing additional expertise, handling specific workstreams or supporting projects where internal resources are constrained. We adapt to your existing workflows and tools. #### --- # https://www.kalvad.com/contributions/ ### Open Source in Numbers 10Maintained Projects100+Upstream Contributions2Sponsored Projects6+Years in Open Source ### Why We Contribute Better EngineeringWorking on open-source projects improves the quality of the software we deliver to clients. We encounter real problems, solve them upstream, and bring those lessons back to every engagement.Giving BackOur work depends on open-source ecosystems. Contributing is one way we help sustain the tools and communities that make our work possible.Continuous LearningCollaborating with maintainers keeps our engineers close to evolving technologies and best practices. We learn from the best and share what we discover.Financial SponsorshipWe provide financial support to open-source ecosystems we depend on, including Gleam, Zig and PyInfra.Community ContributionsBug fixes, documentation improvements, and long-term maintenance for projects like Django, Dramatiq and Alpine Linux.Knowledge SharingWe share our experience through conference talks, engineering guides, and technical discussions in the communities we serve. ### Featured Contribution FOSDEM 2026PyInfra: Because Your Infrastructure Deserves Real Code in Python, Not YAML SoupA talk about infrastructure as code using Python instead of YAML-based configuration. We explain why PyInfra provides better maintainability, testability and flexibility for complex infrastructure automation.View talk details → ### Maintained Projects We maintain and co-maintain production-grade open-source projects that we use in our daily work.Django Cloudflare ImagesBy KalvadIntegrates Cloudflare Images into Django projects.Use case: Automatically optimize images for faster web performance.Django GlideBy KalvadIntegrates Glide.js carousel into Django projects.Use case: Add responsive image carousels to Django websites easily.HushhushBy KalvadPassword-protect your data. A modern, drop-in replacement for privyUse case: Manage password protecting secret dataWoodpecker-opengrepBy KalvadA Woodpecker CI plugin for running OpengrepUse case: Improve your CI pipeline with static code analysis scans.Woodpecker MCPBy KalvadStateless MCP server for Woodpecker CIUse case: Enable AI assistants to manage Woodpecker CI pipelines, repositories, and workflows.InfraninjaBy KalvadAutomates infrastructure setup and common service deployments.Use case: Quickly set up services like Netdata, Docker, and secure environments.LecodeBy KalvadA minimalist terminal AI coding agent. No IDE, no electron, no noise.Use case: Run an AI coding agent in your terminal with any OpenAI-compatible model.Redmine SlackBy KalvadPer-project Slack notifications for Redmine 6.1. Incoming webhooks, zero extra gems.Use case: Slack integration for Redmine issue tracking and wiki updates.Redmine MCPBy KalvadStateless MCP server for Redmine. Each user brings their own API key; no database, no shared secret.Use case: Enable Claude and LLM clients to read/write Redmine issues, wiki, time entries, and more.TaskowlBy KalvadModern Celery task monitoring with MCP integration. No UI, just data.Use case: Monitor and manage Celery tasks, workers, and queues via MCP. ### Core Technical Contributions We contribute to widely-used frameworks and tools that form the foundation of modern software development.Django NinjaA web framework for building APIs with Django and Python type hints.Use case: Fast and type-safe API development for modern web apps.Our contribution: Kalvad helped debug version 1.0 with several patches.KeycloakOpen-source identity and access management solution.Use case: Secure user authentication and authorization for modern apps.Our contribution: Kalvad contributed Arabic language support.MinioOpen-source object storage compatible with Amazon S3.Use case: Self-hosted cloud storage with S3 API compatibility.Our contribution: Kalvad contributes to core Python and .NET libraries.PyInfraTurn Python code into shell commands for remote servers.Use case: Automate server provisioning and operations at scale.Our contribution: Kalvad supports core library development and funding.KanchiReal-time Celery monitoring web application.Use case: Monitor celery tasks in real time in production.Our contribution: Kalvad supports core library development. ### Community & Infrastructure Support We support the infrastructure and communities that power modern software development.Alpine LinuxA lightweight, security-oriented Linux distribution based on musl libc and busybox.Use case: Ideal for minimal container images and secure deployments.Our contribution: Kalvad debugs applications and promotes it through the DXB LUG.ArchlinuxA flexible Linux distribution following the Keep It Simple philosophy.Use case: Used by developers for bleeding-edge packages and custom setups.Our contribution: Kalvad contributes through documentation, mirrors, and debugging.DjangoA high-level Python web framework for rapid, clean development.Use case: Used for building robust web applications quickly.Our contribution: Kalvad provides core contributions and patches.DramatiqA fast, reliable distributed task processing library for Python.Use case: Process background jobs efficiently in production systems.Our contribution: Kalvad contributes to the dashboard, monitoring, and testing.FreeBSDAn advanced OS for servers, desktops, and embedded platforms.Use case: Power reliable infrastructure and high-performance systems.Our contribution: Kalvad members contribute to packages and ZFS development.GarageAn open-source distributed object storage for self-hosting.Use case: Store and manage files reliably in a self-hosted environment.Our contribution: Kalvad tests edge cases and improves reliability.KestraAn open-source data orchestration and scheduling platform.Use case: Replace Jenkins, Airflow, or Prefect for workflow automation.Our contribution: Kalvad developed Pulsar integration and promotes adoption.QuickWitA high-performance, low-latency open-source search engine.Use case: Index and search large datasets quickly.Our contribution: Kalvad contributes through documentation, support, and testing.Warp10A platform for managing and analyzing time series data.Use case: Collect and analyze sensor and IoT data efficiently.Our contribution: Kalvad develops proxies for a compatibility layer. ### Ecosystem Support & Financial Contributions We provide financial support to open-source projects and ecosystems we depend on.GleamA statically typed language for the Erlang VM.Use case: Build scalable, concurrent applications efficiently.Our contribution: Kalvad supports core library development and provides funding.ZigA general-purpose language and toolchain for robust software.Use case: Write fast, safe, and maintainable applications.Our contribution: Kalvad provides financial contributions. ### Our Contributions Include Maintaining and co-maintaining production-grade open-source projectsRegular upstream contributions to widely used frameworks and toolsBug fixes, performance improvements, and long-term maintenanceFinancial support for open-source ecosystems we depend on #### --- # https://www.kalvad.com/guides/on-premise-production/ ControlData, keys, hardware, runtime, and logs stay with the team that owns the outcome.AutonomyNo vendor cliff, no pricing surprise, no forced migration. You move when you choose.CostCapital expense once, operational expense flat, savings compound. Renting forever is the most expensive way to run a stable workload.StrategySkills stay inside the team, optionality inside the business, resilience inside the geography you operate in.How Kalvad helpsWe design, build, and operate self-hosted production stacks for teams that want control without the vendor tax: architecture review, hardware sizing, migration off cloud or SaaS, and the slow boring work of running it afterwards. We do this for ourselves, and for clients in finance, healthcare, and government. ### The Manifesto Infrastructure is a strategic asset, not a rented service. The cloud sold convenience and most teams bought dependence. Self-hosting is the conscious choice to take that dependence back. Four pillars guide how we build production systems.ControlYour data does not leave the building unless you ship it. Your keys do not live in someone else's KMS. When a regulator or auditor asks where something lives, the answer is a sentence, not a 40-slide diagram. Control is also the prerequisite for the other three: without it, a counterparty can take autonomy, cost discipline, and optionality away from you on a Tuesday.AutonomyNo vendor cliff. No pricing update effective next month. No 18-month deprecation notice. Every dependency you accept narrows the decisions you can still make alone; every one you remove widens them. A team running its own CI, logs, Git, and deploys can change its mind about geographies, partners, and products. A team on managed services has its options defined by someone else's board meeting.CostRenting forever is the most expensive way to run a stable workload. Hardware depreciates once; subscriptions compound. Cost is also the opportunity cost of fighting platform limits and the tax of staying compliant with a tool that outgrew its purpose. Self-hosting consolidates all of it into one number you can see and improve. The figures are further down; the principle is predictable and capped.StrategySelf-hosting keeps three things inside the business: skills, because operators learn the systems they touch; optionality, because nothing is locked behind a contract you cannot exit; resilience, because production is not gated by submarine cables or a hyperscaler's region choices. An early company can rent everything and move fast. A growing one that rents everything finds its strategic surface belongs to its providers. ### Who Should Self-Host? The manifesto is universal. The application is not. Use the lists below as a quick filter before the technical chapters.Self-hosting fits whenWorkloads are predictable: builds, internal services, line-of-business apps, scheduled pipelinesData sovereignty or regulator pressure makes physical control non-negotiableThe team has, or wants to grow, real infrastructure skillsCost stability matters more than elastic burst capacityLatency to hyperscalers is a tax where you operateStick with cloud whenYou are early-stage and the product itself is the experimentYou truly need multi-region from day one (most do not)You cannot hire or grow operators and have no appetite to tryWorkload is genuinely bursty: a quarterly batch, a seasonal spikeThe pattern we see working: self-hosted for the steady core, cloud for the experimental edge. Most of the bill, most of the data, and most of the operational pain lives on the steady core. Owning it is where the leverage is. ### Why This Matters for AI AI does not run on a clever model alone. It runs on your data, and it needs that data continuously, not once. Every assistant, every agent, every retrieval pipeline is only as good as the corpus it can reach: your tickets, your wiki, your logs, your code, your history.If that data lives in someone else's SaaS, your AI future is rented too. You get whatever API, rate limit, and export format the vendor decides, and you help train their model on the way. When you host the data, you have privileged and durable access to it. You point a model at it on your terms, build the integrations you need, and keep doing it as models change. The data stays; the model is swappable.A concrete example. Because we self-host Redmine, we built a Redmine MCP server. Our internal assistants read and update projects, issues, and time entries directly, behind our own authentication and audit, with nothing leaving the building. That integration exists because we own the system on both ends. On a hosted tracker we would be waiting for a vendor to ship it, or doing without.This is the compounding edge inside the strategy pillar. Owning your data today is what makes the AI you build on it tomorrow yours, not a feature someone else can price, throttle, or deprecate.Deep dive:the Redmine MCP server we built.From manifesto to migrationThe rest of this page is the practical stack we use to honor the four pillars. Happy to talk through how it applies to your environment.Talk to our engineering team ### The Stack We Run Every component below is open source, self-hosted, and chosen to honor the four pillars. Each solves a specific problem with no vendor relationship attached.The Kalvad self-hosted production stack: Forgejo, Woodpecker, Kamal, Coolify, and OpenObserve on Alpine Linux and FreeBSD.Source and deliveryForgejoGit hosting and code reviewWoodpeckerCI/CD pipelinesKamalContainerized deploys at scaleCoolifyHeroku-style PaaS for simpler deploysObservabilityOpenObserveLogs, metrics, traces, SIEM backendGlitchTipError and crash trackingMonitService supervision and restartsGatusUptime checks and status pagentfy.shPush notifications and alertsNetwork and accessHAProxyLoad balancer and reverse proxyHeadscaleSelf-hosted mesh VPN control serverBlockyDNS with ad and tracker blockingKeycloakIdentity, SSO, and OAuthVaultwardenPassword and secret vaultAIllama.cppLocal LLM inferenceOpen WebUIChat UI for local modelsNanobotMCP agent runtimeData and automationKestraWorkflow and data orchestrationNode-REDFlow-based automationMetabaseBI dashboards and analyticsPocketBaseApp backend: DB, auth, APIDatabasusAutomated database backupsKnowledge and collaborationOutlineTeam wiki and knowledge baseGhostBlog and publishingRedmineProject and issue trackingCampfireTeam chat, once.comDeep dives:Woodpecker Vs Github Actions,Why we chose Woodpecker CI,the 5 stages of monitoring,a local-LLM meeting machine. ### Hardware Strategy Enterprise hardware is a trap: redundant power supplies, hot-swap drives, five-year warranties, approved hardware lists maintained by people who have never logged into a server. We split hardware by the job it does, and each side is cheap, understood, and replaceable.Firepower: commodity mini PCsCompute is stateless and disposable. Our default is 4x Beelink SER5 mini PCs (AMD Ryzen 7 6800H, 32GB RAM, 2TB NVMe), roughly $1,300 total, in a GeeekPi 8U cabinet. One master node runs orchestration and the cache layer; the rest are pure compute. A dead box is reimaged from a spare in five minutes, CARP picks up failover, no RMA and no vendor call.Storage: a FreeBSD rack serverState lives on a rackspace server running FreeBSD on ZFS. No hardware RAID card, on purpose: ZFS wants raw disks, and a RAID controller hides the very errors ZFS exists to detect and repair. Redundancy, checksums, snapshots, and compression are the filesystem's job, not a card that fails silently and ships its own firmware bugs.Four Beelink SER5 mini PCs in a GeeekPi 8U cabinet for compute, paired with a FreeBSD ZFS server for storage.Replaceability over reliability. Enterprise hardware promises five nines, then makes you wait six weeks for an RMA. We take the opposite bet: assume everything fails, make replacement trivial, and let architecture (failover, ZFS, fast reimage) provide the uptime instead of a warranty card.The mini PCs draw less power than one 1U enterprise box: about $15/month in electricity, under two square feet, negligible heat. An equivalent always-on AWS deployment runs $500+/month with no hardware to show for it after three years. ### Operating System Strategy We run exactly two operating systems in production: Alpine Linux for stateless workloads, FreeBSD for anything stateful. Everything else is excluded by default.Alpine Linux is under 100MB, boots in under three seconds, uses musl libc, and refuses systemd. The killer feature is diskless mode: the OS runs from RAM with only the bits you choose persisted via lbu, so any rogue change or accidental rm is gone on reboot. A fully operational Docker host fits in 175 to 350MB of RAM. Use it for CI runners, container hosts, edge networking, cache proxies: cattle, not pets.FreeBSD is what we reach for when data integrity or networking matter. ZFS gives copy-on-write, snapshots, and compression without a hardware controller. PF is a firewall a human can read. pkg with poudriere covers binary and custom builds. sysrc keeps configuration in one place that survives upgrades. Use it for storage nodes, load balancers, edge routers.What we do not run: Debian and Ubuntu (backported into incoherence, plus auto-starting services we never asked for), RHEL and CentOS-likes (outdated packages, subscriptions, RPM), NixOS (learning curve costs more than it saves at our scale), anything with systemd by default, anything that ships snap or flatpak.Measured outcomes from our migration:Storage footprint down 25 percent (ZFS compression, no useless packages)Known vulnerabilities down 50 percent (no systemd, minimal install)Maintenance time roughly halved (less magic, better tooling)Deep dives:declarative Linux,our Linux/Unix journey. ### Cost Comparison: 3-Year TCO A mid-size team with stable CI/CD load, predictable compute, and reasonable data retention. The numbers are from a real migration we ran.3-year total cost of ownership: self-hosted ($17,640) versus cloud ($24,000), break-even in roughly three months.Cost categoryCloud (GitHub Actions + AWS)Self-Hosted (Woodpecker + bare metal)Hardware and compute$18,000 (3 years of runners)$1,300 (one-time, 4x mini PCs)Storage$3,600 (S3, EBS)$800 (local NVMe, one-time)Data transfer$2,400 (egress fees)$0 (local network)Electricity$0 (in the cloud bill)$540 ($15/month, 36 months)Operations$0 (outsourced, and the judgment)~$15,000 (0.2 FTE over 3 years)Total (3 years)$24,000$17,640Break-evenN/A~3 monthsThe honest version: the $15K of operations is not lost. It is a person on your team learning your systems, instead of a vendor learning them for you. After three years you have hardware on a shelf, code in a repo, and operators who can debug an incident without filing a ticket. ### Security: Process Over Compliance Security is a never-ending process, not a validation step. Not a form for an insurer once a year, not an ISO certificate on the wall, not a WAF you bought because PCI 6.6 told you to. It is the boring, repetitive work of reading changelogs, patching servers, killing dead services, rotating credentials, and refusing the shortcuts everybody wants to take.WAFs are mostly theater. They are either in log-only mode (useless), in blocking mode permissive enough to wave through a forged user agent (useless), or blocking legitimate traffic and generating support tickets (worse than useless). Every dollar on a WAF is a dollar not spent on code review, dependency scanning, SAST, DAST, or a good pentester. PCI 6.6 lets you choose. Code review is cheaper, more effective, and makes the product better.Alert on what matters, log everything else. We only page on a successful attack or a credible indicator of one. Failed logins, denied packets, and scanner noise flow into OpenObserve, all queryable, none of it waking anyone. Train the team to read pages by making pages worth reading.Deep dive:Security is a never-ending process. ### Compliance Without Cosplay Most teams treat compliance like a sacred text, citing ISO 27001, PCI DSS, and SOC 2 as if carved in stone, then refusing reasonable changes because "the auditor would never accept that". Read the standards instead. They contain risk-based logic and emergency clauses. They are frameworks, not laws.The right answer to "show us your asset inventory" is a query against Jinn, not a spreadsheet last accurate in 2023. To "show us your patch SLA," a chart from OpenObserve. To "who has admin access," the output of a PyInfra script. When you own the inventory, the deploy pipeline, and the logs, every audit artifact is one query away. The team that automates compliance ships fast and passes audits. The team that types it into Word twice a year does neither.Deep dive:Compliance standards are not carved in stone. ### Operating Principles The four pillars set direction. These principles are how the work gets done day to day, and they are what survived a decade of pulling things out of production.Simple tools, deep understanding. A FreeBSD box with pf, configured by an operator who reads the logs every week, outperforms a misconfigured $200K firewall stack. Simple tools fully understood beat complex tools half understood, every time.Security engineers must code. Attackers write code; their payloads and C2 are code. A defender whose main output is a quarterly slide deck is not on equal footing.Skills over certifications. Every SaaS tile quietly erodes the skills the industry was built on. Running your own infrastructure puts them back: how containers actually work, networking, storage performance, capacity planning, real hardening. These do not get deprecated next version.AI as tool, not crutch. Use it for log triage, anomaly summaries, detection rules, and postmortems. Do not trust it blindly, and do not let the team forget how to work without it. We run Qwen locally on a single GPU: transcripts stay in the building, cost is fixed, latency predictable.Deep dive:DevOps in 2025: the pauperization machine. ### The Honest Trade-offs Self-hosting is not all sunshine. Pretending otherwise is how teams get sold the next round of managed services.You are the support team now. When something breaks at 11 PM, there is no ticket and no five-day SLA. You fix it. For some teams that is the point; for others it is terrifying. Both reactions are valid signals.Updates are your responsibility. Read the changelog, test in staging, deploy. With Coolify or Kamal and PyInfra this is mostly painless, but it is still work.Initial setup takes time. About two weeks to get runners, registry, cache, monitoring, alerting, and deploys clean. Two weeks once, instead of fighting a vendor forever.You need actual skills. Linux, networking, containers, sysadmin. If the team reflex is "restart the pod", that is a hiring problem to solve first.Hardware fails. Architecture and spare boxes handle this. A vendor warranty does not.Internet outages affect you differently. Local builds keep running; cloud builds wait for the link. Depending on where you operate, a win or a wash. ### Best Practices What actually works in production, after running this stack across multiple clients:Start small, show wins. Monit in week one, centralized logs in month one, traces in quarter one. Each step earns the next.Log everything, alert on what matters. Storage is cheap, attention is not. Years of logs, zero pages for noise.Automate everything you can debug. If you cannot explain an automation at 3 AM, it owns you. Write code you can reason about under stress.Keep it boring. Fewer moving parts, fewer 2 AM failures. Unix boxes, code that deploys to them, a small number of tools you understand top to bottom.Measure p99, not average. Users remember slow requests, not fast ones.Fail loud. When retrieval misses or a dependency is unreachable, say so. Do not paper over it.Match architecture to actual load. Most teams do not need a service mesh. Add complexity when failure data demands it, not before.Common objections we hear"It is too expensive." Compared to the last outage or the next price hike, it usually is not. Most clients break even inside the first year."We do not have time." The time goes somewhere. Today it fights someone else's tooling. We have measured 10 to 15 hours a week recovered on mid-size teams."The cloud handles it." Cloud providers monitor their infrastructure, not your code, data, or business logic. The shared-responsibility model puts the parts that matter on you."Developers will resist." The most common post-migration feedback is "why did we not do this sooner?". ### FAQ What is self-hosted production?+Running the services your business depends on, on hardware and software you control end to end. Data, compute, deploy pipeline, logs, and configuration all live on infrastructure you own, not rented from a cloud provider or SaaS vendor.Is self-hosting cheaper than cloud?+For stable, predictable workloads, yes. Our 3-year TCO for a mid-size team comes out around 25 percent lower than the equivalent GitHub Actions plus AWS setup, break-even in roughly three months. It is not cheaper for bursty or experimental workloads.What is a good self-hosted alternative to GitHub Actions?+Woodpecker CI paired with Forgejo for Git hosting. Woodpecker is Apache 2.0, community-driven, with YAML pipelines you can actually read. Average build time after migration dropped from 13 minutes to under 3.Should I use Coolify or Kamal?+Pick one by scale and team preference. Coolify is a Heroku-style platform with a UI, right for simpler and smaller environments. Kamal is CLI-first, code-defined, from 37signals, for at-scale production where the deploy flow lives in version control. We do not run both on the same target.Do I need Kubernetes to self-host production workloads?+No. Most teams do not. We run Alpine and FreeBSD on commodity hardware, with Coolify, Kamal, and PyInfra. Kubernetes fits a narrow set of problems and most workloads we see are not in it.Is self-hosted infrastructure more secure than cloud?+Security depends on the competence of the people running the system, not the location of the servers. Self-hosting forces you to understand your own stack, usually a security upgrade. Cloud security is only as good as how well you configured the cloud.How do you handle disaster recovery?+Spare identical hardware on a shelf, configuration in a Git repo, deploys automated by PyInfra plus Coolify or Kamal, ZFS snapshots for stateful nodes. A failed mini PC is replaced in about five minutes by running the same deploy that built the original.Does self-hosting help with ISO 27001, SOC 2, or PCI DSS?+Yes. When you own the inventory, the deploy pipeline, and the logs, every audit artifact is one query away. We generate asset inventories from Jinn, patch SLAs from OpenObserve, access lists from PyInfra. Auditors prefer real evidence to maintained spreadsheets.What hardware do you recommend?+Four Beelink SER5 mini PCs (AMD Ryzen 7 6800H, 32GB RAM, 2TB NVMe each) for compute, plus a FreeBSD rack server on ZFS for storage, about $1,300 for the mini PCs. One master node runs orchestration and the cache layer, the rest are pure compute.Ready to own your stack?We design and operate production-grade self-hosted systems for teams that want control, autonomy, predictable cost, and strategic optionality. Hardware, OS strategy, CI/CD, observability, security, and the slow boring work afterwards.Talk to us about your infrastructure #### --- # https://www.kalvad.com/guides/rag-architecture-production/ ## RAG Architecture in Production RAG (Retrieval-Augmented Generation) is one of the most effective ways to build production AI systems on top of your own data. This guide breaks down how RAG architectures work in practice: components, trade-offs, performance constraints, and real-world deployment patterns. ### On this page What is RAG?When to Use RAG vs Fine-TuningCore Architecture OverviewProduction ChallengesBest PracticesCost and Infrastructure ### What is RAG? RAG (Retrieval-Augmented Generation) is an architecture that combines retrieval systems with language models to generate responses grounded in external data. In practice, RAG is a pipeline: chunk documents into manageable pieces, embed those chunks into a vector space, retrieve the most similar chunks to a user query, and feed them to the model alongside the question. The result is responses grounded in your actual data rather than the model's training corpus. ### RAG Architecture at a Glance Combines retrieval systems with LLMs to ground responsesUses vector search to find relevant contextImproves accuracy, reduces hallucinationsRequires careful handling of latency and evaluation Deep dive: RAG Core Components ### When to Use RAG vs Fine-Tuning This is the first architectural decision, and most teams get it wrong. The common misconception is that fine-tuning teaches the model new knowledge or controls its style. In practice, fine-tuning serves a different purpose entirely. Use RAG when:Your data is private, proprietary, or specific to your organizationInformation changes frequently (policies, products, documentation)You need source attribution and verifiable responsesYou require traceability and auditabilityYou cannot afford to retrain a model every time something changes Use fine-tuning when:You operate at massive scale (millions of API calls) and need to reduce per-query token costsLatency is critical and you need to eliminate lengthy system promptsYour domain has highly specialized jargon, codes, or terminology that the base model consistently misreadsYou want to distill a smaller model to behave like a larger one for a specific taskYou have a high-volume, repetitive task requiring deterministic output The pattern: fine-tuning optimizes for efficiency and consistency at scale, not for knowledge injection or output style. A common myth: Fine-tuning is often pitched as the way to control tone, voice, or output format. In practice, a well-crafted system prompt handles those requirements without the cost and maintenance overhead of training. Fine-tuning for style is almost always the wrong tool. Use both when: You have frequently changing data (RAG) and high enough scale to justify a fine-tuned generation model for cost efficiency. This combination is rare but valid for high-throughput production systems. Deep dive: The RAG Decision: When to Choose RAG Over Fine-Tuning ### When RAG Isn't the Right Tool RAG is not a universal foundation for AI systems. Treating it as one is a common architectural mistake. RAG is purpose-built for unstructured document retrieval: policies, manuals, knowledge bases, articles, support documentation. When the question is "what does our documentation say about X," RAG is the right pattern. RAG is the wrong tool when:Data is structured (databases, APIs, CRMs). Tool calling and direct query translation produce more accurate, deterministic results than embedding rows and searching for them.Documents are already in hand. If the user has uploaded a single contract or report, just pass it to the LLM directly. Chunking and embedding a single document adds complexity without benefit.Questions require computation, aggregation, or filtering ("how many tickets did we close last quarter"). These are SQL queries, not retrieval problems. The right architecture often combines patterns: RAG for unstructured corpora, tool calling for structured data, direct context loading for individual documents in scope. Choosing the right pattern per data type is more important than picking a single approach for the whole system. ### Core Architecture Overview A production RAG system has five core stages: Ingestion . extracting and cleaning data from source systemsEmbedding . converting chunks into vectorsRetrieval . finding relevant chunks for a queryOrchestration . routing, reranking, and context assemblyGeneration . producing the final response Each stage has different scaling characteristics, failure modes, and latency profiles. Treat them as independent components that can be swapped, not a monolithic pipeline. Deep dive: RAG Architectures - From Basic to Big Brain Mode #### Ingestion Pipeline The retrieval system is only as good as what's indexed. Ingestion is where most production RAG systems fail. Chunking strategy matters more than embedding model choice. The Goldilocks zone for most use cases is 200-500 tokens per chunk, large enough to preserve meaningful context, small enough to keep embeddings focused. Q and A over documentation typically performs best at 256-512 tokens. Legal, compliance, and code documentation may benefit from larger chunks (512-1024 tokens) to preserve full clauses or function definitions. Structured data often should not be chunked at all. Direct query translation works better. Data quality enforcement is critical. Garbage in, garbage out applies absolutely to RAG. If low-quality content reaches the index, retrieval cannot clean it up. Implement validation at ingestion: detect duplicates, flag low-quality content, normalize encoding issues, strip headers and footers, and extract metadata for filtering. Update cadence depends on use case. Some systems need near-realtime indexing; others can batch daily or weekly. Do not over-engineer indexing speed if updates are infrequent. Match the pipeline to the actual rate of change. Deep dive: Chunking: The Foundation of Retrieval Quality #### Embeddings The embedding model converts text into vectors. These vectors define the search space for retrieval. Model selection: BGE, OpenAI's text-embedding-3 family, Cohere's embed-v3, and Google's text-embedding-004 all perform well across most use cases. The recall difference between top performers is small in production. Consistency, latency, and operational fit usually matter more than marginal benchmark differences. Dimension count: Higher dimensions can improve recall but increase storage cost and retrieval latency. The right dimension count depends on corpus size and query patterns; benchmark on your own data rather than defaulting to whatever the API offers. Normalization: Most modern embedding models produce normalized vectors. Confirm this in the model documentation. If normalization is required and not applied, cosine similarity calculations break. Dense vs sparse: Dense embeddings capture semantic similarity. Sparse methods (BM25, SPLADE) handle exact keyword matching, proper nouns, and rare terms. Hybrid approaches combining both typically outperform either alone in production, at the cost of additional operational complexity. Deep dive: Embeddings & Vector Databases #### Retrieval Layer Retrieval is often the bottleneck. The most common failure mode is over-fetching, returning too many chunks, which dilutes relevance and inflates generation latency. Top-k tuning: Do not default to k=10. The right k depends on chunk size and question complexity. For most systems, k=3-5 final chunks sent to the LLM is sufficient. More chunks introduce more noise into the context window. Threshold-based filtering: Instead of (or alongside) top-k, filter by similarity score threshold. This handles edge cases where relevant documents simply do not exist in the index. Better to return nothing than return irrelevant matches. Reranking: Two-stage retrieval is the production standard. Retrieve a broader set of candidates with a fast bi-encoder (vector search), then rerank with a cross-encoder model that scores query-document pairs precisely. This combines the scalability of vector search with the accuracy of cross-encoding. Query understanding: User queries are often short, ambiguous, or use different vocabulary than the indexed documents. Query rewriting, expansion, decomposition, and HyDE (hypothetical document embeddings) can substantially improve retrieval quality before the search even runs. Deep dive: Query Processing: Getting the Question Right #### Orchestration Orchestration is the control layer between retrieval and generation. It decides what context is sent to the model and how. Context assembly: Assemble retrieved chunks into a structured prompt. Include document citations for traceability. Chunk ordering matters. Chronological order often produces better answers than relevance order for multi-document questions. Handling retrieval failures: When no relevant chunks are found, the system should clearly indicate that the answer is not available in the knowledge base rather than hallucinate from the model's training data. For high-stakes use cases (legal, medical, financial), saying "I don't know" is the correct behavior. Falling back to general model knowledge should be an explicit, marked decision, not a default. Multi-turn conversation: Chat history changes the retrieval target. Approaches include rewriting the current query using prior turns, retrieving across the full conversation, or using specialized memory architectures. Choose based on actual conversation patterns observed in production, not theoretical elegance. Guardrails: Input filtering (prompt injection prevention), output filtering (sensitive content blocking), and query classification (routing to appropriate handlers) are not optional in production deployments. #### Generation The generation model produces the final response. Model selection here involves different trade-offs than embedding or reranking. Model size: Larger models follow instructions better and use context more reliably, but cost more and run slower. For many RAG use cases, a mid-sized model with strong context handling outperforms a larger model without RAG. Strong retrieved context compensates for raw model capability. Context window: Newer models offer 128K+ token context windows. This enables longer contexts but introduces challenges. Attention complexity grows quadratically, and effective context utilization degrades well before the theoretical limit. Test actual performance, not advertised capacity. Temperature and sampling: For factual RAG responses, use low temperature (0.0-0.3). Higher temperatures introduce variability that often manifests as hallucination, even with strong retrieval. Reserve higher temperatures for creative or exploratory use cases. Structured output: When you need JSON or a specific format, use models with native structured output support or apply schema validation. Do not rely on prompting alone for output structure. ### Building a RAG system? We design and operate production-grade RAG systems for organizations that need reliability, not prototypes. We've shipped production RAG systems for government entities and enterprises where accuracy, compliance, and uptime are non-negotiable.Talk to our engineering team ### RAG Architecture Patterns Not all RAG systems are built the same way. Different query patterns and accuracy requirements call for different architectures. Understanding the patterns helps you match complexity to the actual problem. #### Naive RAG The starting point. A single retrieval step feeds the LLM, which generates the response. No reranking, no query rewriting, no self-correction. When to use: MVP and prototype phases, simple factual Q and A over a clean knowledge base, predictable query patterns, low-latency requirements. Limitations: Cannot handle complex multi-step questions, no recovery when retrieval fails, single strategy applied to all query types regardless of fit. Naive RAG handles a surprisingly large share of real-world queries. Do not add complexity until production data shows it needed. #### Agentic RAG An LLM-controlled retrieval loop. Instead of a fixed pipeline, the LLM decides what to retrieve, when to retrieve it, whether to retrieve at all, and when it has enough information to answer. The model becomes an agent reasoning over the retrieval process itself. When to use: Open-ended research assistance, complex multi-hop questions, exploratory analysis, scenarios where queries require different retrieval strategies that cannot be predetermined. Trade-offs: Higher latency from multiple LLM calls, harder to debug, more expensive per query. The flexibility costs predictability. #### Corrective RAG (CRAG) Adds an evaluation step after retrieval. A relevance evaluator scores the retrieved documents. If they pass, generation proceeds normally. If they fail, the system triggers a fallback (web search, alternate retrieval strategy, or refusal to answer). When to use: Legal, medical, financial, and compliance contexts where wrong answers are unacceptable. Situations where saying "I don't know" is preferable to a confident but wrong response. Trade-offs: Extra LLM call adds latency and cost. The accuracy gain is significant for high-stakes use cases but unnecessary for casual ones. #### Adaptive RAG A query classifier routes incoming requests to the most appropriate retrieval strategy. Simple factual queries go through a fast naive path. Complex comparisons trigger query decomposition. Critical questions route through CRAG. Greetings skip retrieval entirely. When to use: Production systems serving diverse query types, particularly when query volume is high enough that optimizing the average case yields meaningful cost and latency improvements. Trade-offs: More moving parts, more pipelines to maintain, classifier accuracy becomes a critical dependency. #### GraphRAG Replaces (or augments) vector similarity with knowledge graph traversal. Documents are processed into entities and relationships; retrieval follows graph edges rather than vector distance. When to use: Questions where entity relationships matter: organizational structures, regulatory dependencies, citation networks, supply chains, anything where the answer requires understanding how things connect rather than what they say. Trade-offs: Significantly higher ingestion complexity (extracting entities and relationships from documents), graph database operational overhead, harder to update incrementally. Choosing an ArchitectureSituationArchitectureKey BenefitPrototype or MVPNaive RAGSimplicity, fast to shipMostly simple queriesNaive RAGLow latency, low costAccuracy is criticalCRAGSelf-correction, safe fallbacksComplex, exploratory queriesAgentic RAGDynamic reasoningDiverse query types at scaleAdaptive RAGOptimized per queryEntity relationships matterGraphRAGConnection-aware retrieval The right pattern is the simplest one that solves your actual problem. Add architectural complexity only when production failures justify it. ### Production Challenges Four challenges trip up most production RAG deployments: #### Latency End-to-end latency is the sum of retrieval (milliseconds), reranking (milliseconds to a second), and generation (typically seconds). The slowest component sets the SLA. For interactive use cases, target under 2 seconds total. Profile each component independently. Retrieval is often faster than expected, generation is often slower. #### Evaluation RAG evaluation differs from standard LLM evaluation. The system has two stages that fail independently, so both need separate quality measurement. Retrieval metrics:Recall: did we find all relevant documents?Precision: of what we found, how much was actually relevant?MRR (Mean Reciprocal Rank): how high in the ranking is the best document?NDCG (Normalized Discounted Cumulative Gain): overall ranking quality across multiple relevant documents Deep dive: Advanced retrieval Generation metrics:Faithfulness: is the answer grounded in retrieved documents, or did the model hallucinate?Relevance: does the answer actually address the question?Completeness: did the answer cover everything needed?Conciseness: is the answer focused, or padded with unnecessary content? LLM-as-judge approaches work for automated evaluation but require careful prompt design to avoid bias. Combine with periodic human evaluation for ground truth. Deep dive: How to Know If Your RAG System Is Actually Good (And Keep It That Way) #### Hallucinations RAG reduces hallucinations but does not eliminate them. The model may ignore retrieved context, conflate details across chunks, or generate from its own training data. Mitigations: enforce citations in the output, verify generated claims against retrieved content, and test faithfulness as a primary metric, not just "does the answer look right." #### Scaling Vector search scales sub-linearly with index size but linearly with query rate. For high-volume systems, the embedding and generation models become the bottleneck (GPU compute), not the vector store. Plan for horizontal scaling of inference endpoints separately from the vector database. Caching at every layer (embeddings, retrieval results, generations) typically yields larger gains than infrastructure tuning. ### Best Practices What works in production: Start with chunking. The right chunking strategy improves retrieval more than any embedding model upgrade.Use two-stage retrieval. Retriever plus reranker outperforms single-stage retrieval for any precision-critical use case.Measure end-to-end latency at p99, not average. Tail latency matters most for interactive systems.Fail gracefully. When retrieval fails, the system should say so, not hallucinate.Build evaluation before optimization. You cannot improve what you do not measure.Log and review edge cases. Failed queries reveal systemic issues faster than aggregate metrics.Do not optimize the embedding model before the retrieval pipeline is stable. Retrieval issues compound downstream and obscure the impact of model changes.Match architecture to query patterns. Most systems do not need GraphRAG or full agentic loops. Add complexity when failure data demands it. ### Cost and Infrastructure RAG infrastructure costs are distributed across components. The deployment model determines the cost shape. Cloud (managed services): Vector databases (Pinecone, Weaviate, Azure AI Search), embedding APIs (OpenAI, Cohere, Google), and model APIs are the fastest path to production. Costs scale with usage: easy to start, potentially expensive at scale. Managed services handle scaling, availability, and updates. The trade-off is reduced control and vendor lock-in. On-premise (self-hosted): Open-source embedding models, open-source vector stores (Qdrant, Milvus, pgvector, ChromaDB), and self-hosted LLMs offer full control. Costs are upfront hardware plus operations rather than per-query. The trade-off is required infrastructure expertise and slower iteration. Hybrid: A common production pattern: on-premise embedding and reranking (where data sensitivity matters), cloud LLMs (where cost-per-query is acceptable). This balances data control with operational cost. Cost optimization paths, ordered by typical impact:Reduce chunk size (fewer input tokens per query)Optimize retrieval k (fewer chunks sent to the LLM)Choose a smaller generation model (lower per-call inference cost)Batch requests where latency permits (higher throughput per GPU)Cache aggressively (eliminate redundant computation) ### Ready to build production RAG? We design and operate production-grade RAG systems for organizations that need reliability, not prototypes. We've shipped production RAG systems for government entities and enterprises where accuracy, compliance, and uptime are non-negotiable.Talk to our engineering team #### --- # https://www.kalvad.com/guides/ RAG Architecture in ProductionA practical guide to building production-grade RAG systems. Covers architecture, components, evaluation, latency optimization, and real-world trade-offs.Read RAG guideOn-Premise ProductionLearn why leading organizations are bringing infrastructure back in-house: cost control, data sovereignty, and operational excellence without vendor lock-in. Real-world patterns from production deployments.Read on-premise guide #### --- # https://www.kalvad.com/hadir-attendance/ Replace manual attendance with precise, geofenced tracking. Hadir makes workforce management effortless. ### Why Hadir? #### Flexible Deployment Works fully standalone or integrates with existing HRM systems. #### Real-Time Insights Track attendance, shifts, and location events as they happen. #### Accurate Attendance Eliminate errors from manual tracking with automated geofenced check-ins. #### Custom Pages Add your own content like About Us, Terms & Conditions, or company policies. #### Multilingual Support Mobile app available in English, Arabic, and additional languages. #### Enterprise Authentication Integrates with LDAP, Active Directory, Microsoft 365, and other authentication systems. #### Custom Branding Tailor the app's logo, colors, and appearance to your company style. #### Chatbot Integration Provide an AI-powered assistant to answer questions about policies, shifts, and procedures. #### Intuitive Experience Simple for admins and employees alike. ### Admin Panel - Full Control at Your Fingertips Hadir's Admin Panel empowers managers to monitor, manage, and analyze workforce activity from a single dashboard. #### Login & Authentication Secure administrator access with support for enterprise authentication systems including LDAP, Active Directory, Microsoft 365, or built-in standalone login. #### Dashboard Comprehensive overview of daily check-ins and check-outs, average shift durations by day and week, plus detailed insights into user activity and attendance trends. #### Users Administration Manage employee accounts efficiently by creating, updating, organizing users, and assigning roles and access permissions. #### Location Management Configure geofenced locations, manage temporary or permanent sites, and assign authorized users. #### Attendance Sessions Track and review attendance records in real time, identify behavioral patterns, and ensure policy compliance across your workforce. ### Mobile App - Simple, Powerful, and Accurate The Hadir Mobile App ensures employees can check in and out effortlessly. Available on Android and iOS. #### Android & iOS Available on both platforms for maximum reach. #### Geofenced Check-ins Accurate location tracking for every check-in and check-out. #### Intuitive Interface Simple for daily attendance and shift management. #### Multilingual Support Available in English, Arabic, and additional languages. #### Secure API Communication Secure communication with the backend. ### Integrations - Fit Your Existing Workflow Hadir is designed to fit seamlessly into your current systems. #### Standalone Mode Deploy as a complete solution with no HRM required. #### HRM Integration Sync attendance and location data with Oracle Cloud or other HRM platforms via API. #### Enterprise Authentication Connect with LDAP, Active Directory, Microsoft 365, or other authentication systems. #### Advanced Enterprise Integration Add custom branding and AI-powered chatbot for policy assistance.Advanced Enterprise FeaturesEnable custom branding to tailor the app's appearance to your company identity, and chatbot integration to provide employees with instant guidance about policies, shifts, and procedures.Flexible Deployment: Standalone or HRM integrated.Efficiency: Automates attendance tracking, reducing administrative work.Accuracy: Ensures precise attendance and location data.Insights & Analytics: Gain actionable intelligence on workforce behavior.Custom Content & Pages: Add About Us, Terms & Conditions, or company-specific content.Multilingual Experience: Supports employees in multiple languages.Enterprise Ready: Compatible with corporate authentication systems.Custom Branding: Tailor the look and feel to your company identity.AI-Powered Chatbot: Answer employee questions about policies, shifts, and procedures.User-Friendly & Scalable: Simple for admins and employees, works for organizations of any size. ### How It Works 1 #### Setup Locations Admins define check-in points, geofences, and assign users.2 #### Employee Check-In Employees check in/out via mobile app, automatically capturing location.3 #### Data Sync Attendance events are recorded in real time in the Admin Panel.4 #### Insights Managers access dashboards to track attendance trends, shift lengths, and compliance.5 #### Custom Pages & Multilingual Employees access content like policies in their preferred language.6 #### Optional HRM Integration Automatically sync data with Oracle Cloud or other HR systems. ### Get Started with Hadir Hadir makes workforce attendance accurate, simple, and adaptable.Start Tracking Attendance #### --- # https://www.kalvad.com/hakeem-ai/ AIPROCESSING ##### Purchase Survey The reception staff were exceptionally welcoming and professional, making check-in a breeze with virtually no waiting time. The overall experience exceeded my expectations in terms of both efficiency and friendliness of service. ##### Support Ticket The agent was polite, but the issue remains unresolved after several attempts. The suggested solutions didn't address the core problem, and I'm still experiencing the same error messages. ##### Marketing Response The latest iOS update has significantly improved my device's performance and stability. Not only did they address the scrolling issue I reported, but they also enhanced several other features I use daily, making the experience seamless. ##### Purchase Survey The reception staff were exceptionally welcoming and professional, making check-in a breeze with virtually no waiting time. The overall experience exceeded my expectations in terms of both efficiency and friendliness of service. ##### Support Ticket The agent was polite, but the issue remains unresolved after several attempts. The suggested solutions didn't address the core problem, and I'm still experiencing the same error messages. ##### Marketing Response The latest iOS update has significantly improved my device's performance and stability. Not only did they address the scrolling issue I reported, but they also enhanced several other features I use daily, making the experience seamless. ##### Purchase Survey Positive85 / 100AspectsFront DeskQueue ##### Support Ticket Negative25 / 100AspectsWebsiteBugfix ##### Marketing Response Positive99 / 100AspectsUpdateBugfix ##### Purchase Survey Positive85 / 100AspectsFront DeskQueue ##### Support Ticket Negative25 / 100AspectsWebsiteBugfix ##### Marketing Response Positive99 / 100AspectsUpdateBugfix ### The Feedback Problem Customer feedback is everywhere, but most organizations struggle to extract value from it. Manual analysis is slow, inconsistent, and misses critical patterns. #### Overwhelming Volume Thousands of surveys, tickets, and reviews pour in daily. No team can read them all, so critical issues slip through the cracks. #### Slow Manual Analysis Tagging and categorizing feedback by hand takes days or weeks. By the time insights emerge, the window for action has closed. #### Lost Insights Without systematic analysis, patterns, sentiment shifts, and emerging trends remain hidden in unstructured text. ### Why Hakeem? #### In-House AI Models Built entirely by Kalvad's team for maximum reliability, control, and performance. #### Actionable Insights Structured themes, sentiment scores, and trend data tailored to your business context. #### Expert Support Dedicated team to help integrate, optimize, and continuously improve your results. #### English & Arabic Native multilingual support with accurate sentiment analysis across both languages. #### Real-Time Trends Detect sentiment shifts and emerging issues as they happen, not weeks later. #### API-First Design Connect to any system with comprehensive docs, SDKs, and webhook support. #### Enterprise Security SOC 2 Type II compliant, GDPR ready, with OAuth 2.0 and encrypted data handling. #### Multi-Channel Analyze surveys, tickets, reviews, social media, and more from a single platform. #### Custom Models Train Hakeem on your industry terminology and business-specific categories.Real customer feedback analyzed from a restaurant in Abu Dhabi ### How It Works 1 #### Connect Sources Link surveys, support tickets, reviews, or upload feedback files via API or dashboard.2 #### AI Processing Hakeem's models analyze each response for sentiment, themes, and key topics automatically.3 #### Theme Detection Feedback is organized into actionable categories like "Pricing," "Support Quality," or "Product Bugs."4 #### Sentiment Scoring Each response and theme receives a sentiment score, highlighting what's working and what isn't.5 #### Trend Alerts Get notified when sentiment shifts or new issues emerge across your feedback channels.6 #### Take Action Share insights with your team, prioritize improvements, and track the impact of changes. ### Who Benefits from Hakeem? Hakeem is designed for teams that need to understand customer sentiment at scale and act on it quickly. #### Support Teams Surface top ticket themes and recurring pain points automatically. Reduce response times and improve CSAT by addressing the issues that matter most. #### Product Teams Prioritize features and fixes based on real user sentiment. Understand what customers love, what frustrates them, and where to invest next. #### CX & Marketing Understand voice of customer across all channels. Track brand sentiment, campaign impact, and customer journey friction points in one place.Security & ComplianceHakeem's APIs use OAuth 2.0 and JWT authentication with granular permissions. The platform is SOC 2 Type II compliant andGDPR ready, with all data encrypted in transit and at rest.SOC 2 Type IIGDPR ReadyOAuth 2.0Encrypted Data ### Integrations - Fit Your Workflow Hakeem's API-first architecture connects seamlessly with your existing tools for real-time feedback analysis. #### API Documentation Interactive Swagger/OpenAPI specs with live testing endpoints and code samples. #### Pre-Built SDKs Ready-to-use libraries for Python and Django to accelerate integration. #### Webhook Support Real-time push notifications when new insights are processed and ready. #### Custom Connectors Bespoke integrations for legacy systems, CRMs, helpdesks, and survey platforms. ### Get Started with Hakeem Hakeem transforms raw feedback into clear, actionable insights so your team can make data-driven decisions with confidence.Request Demo #### --- # https://www.kalvad.com/jinn/ Your servers have vulnerabilities right now. Jinn finds and fixes them. ### Why Jinn? #### Automated Vulnerability Scanning Continuous CVE scanning against the full database — even offline. Always know what needs patching. #### One-Click Patch Management Deploy patches across your fleet in minutes. Schedule windows, batch rollouts, and automatic rollback on failure. #### Agentless SSH Architecture No daemons, no packages, no open ports on your servers. Connect over SSH — nothing to install or maintain. #### Compliance Reporting Auto-generated audit-ready PDF reports for ISO 27001, SOC 2, GDPR, PCI-DSS, and HIPAA — in one click. #### Air-Gap Compatible Purpose-built for classified and critical-infrastructure networks. Fully disconnected operation with offline CVE updates. #### Multi-Cloud Support Manage servers across AWS, Azure, GCP, VMware, Nutanix, and bare-metal from a single dashboard. #### CIS Benchmark Hardening Enforce SSH config, kernel parameters, and file permissions fleet-wide with one click. Prove it in your next audit. #### Role-Based Access Control Admins, managers, and engineers each see only what they need. Least-privilege access out of the box. #### Zero-Downtime Patching Roll out patches in batches with built-in health checks. Automatic rollback reverts changes if anything fails. ### Real-Time Vulnerability Dashboard Know the security status of every server without reading a single log file. Jinn distills complex vulnerability data into a clear traffic-light view for technical and non-technical teams alike. #### Live Security Dashboard See every server's patch status, open CVEs, and risk score on a single screen. Filter by severity, team, or environment. #### Automated Compliance Reports Generate audit-ready PDF reports in seconds. Every patch, scan, and configuration change is logged with timestamps and attribution. #### Risk Reduction Metrics Track mean time to patch, vulnerability remediation rates, and overall security posture — board-ready metrics updated in real time. ### On-Premise Security Platform Every component runs inside your network. No telemetry, no cloud callbacks, no third-party data processing. Your vulnerability data stays yours. #### Offline CVE Vulnerability Scanning Scan every package against the full CVE database even without internet access. Update the feed via USB or bastion relay for air-gapped networks. #### 100% On-Premise Deployment Web UI, scanning engine, CVE database, and reporting layer all run on a single VM inside your perimeter. No SaaS dependency. #### Air-Gap Compatible Fully disconnected operation — no DNS lookups, no NTP calls, no outbound traffic. Built for defense and critical infrastructure. #### Agentless SSH-Only Connection Standard SSH keys only. No listening ports, no resident processes, no package dependencies on managed servers. #### Native Cloud Provider Integration Connect AWS, GCP, Azure, VMware, or Nutanix accounts and Jinn automatically imports your server inventory. New instances are detected without manual intervention. #### Single-VM Docker Compose Deployment Run docker compose up on one VM and Jinn is ready. No Kubernetes, no managed database, no load balancer.Powered by Open-Source TechnologyJinn leverages InfraNinja — our open-source infrastructure automation library — for its core connectivity and execution layer.View on GitHub ### See Jinn in Action ### Frequently Asked Questions How quickly can we get started with Jinn?+Most organizations complete their first vulnerability scan within 24 hours. Deploy Jinn on a single VM via Docker Compose, add your servers by IP or import them from your cloud provider, and run a scan. Since Jinn is agentless, there is nothing to install on your servers — just provide SSH access.Does Jinn support hybrid cloud and on-premise environments?+Yes. Jinn manages servers across AWS, Azure, GCP, VMware, Nutanix, private clouds, and bare-metal data centers from a single interface. Patching policies, hardening profiles, and compliance reports work identically regardless of where your infrastructure runs.Is our vulnerability data safe?+Jinn is fully self-hosted — all vulnerability data, scan results, server credentials, and audit logs are stored on your infrastructure and never transmitted externally. Data is encrypted at rest (AES-256) and in transit (TLS 1.3). Kalvad has no access to your data.How does Jinn pricing work?+Jinn is sold as a yearly contract with straightforward per-server pricing and volume discounts for larger fleets. There are no per-user charges, no feature tiers, and no hidden fees — every customer gets the full platform with a dedicated technical account manager. Contact us for a custom quote.Can I try Jinn before purchasing?+Yes. We offer a personalized live demo where our engineering team walks you through the platform using your infrastructure requirements. Book a demo and we'll tailor the session to your environment. ### Get Started with Jinn Stop patching manually. Start securing automatically. See how Jinn can cut vulnerability remediation time by 80%.Request Demo #### --- # https://www.kalvad.com/laroute/ One OpenAI-compatible API for cloud and private models. Laroute brings keys, limits, logs, and usage reports to your own infrastructure. ### Why Laroute? #### One API, Every Model Use one base URL and key format across all connected providers. #### Cloud & Private Providers Connect OpenRouter, OpenAI-compatible services, and private vLLM servers. #### Keys & Spending Limits Create virtual keys with expiry dates and per-period budgets. #### Team Sharing Shared app accounts with their own keys, budgets, and request history. #### Full Request Logs See model, key, result, token use, cost, and response time per request. #### AI Usage Analytics Detect tools like Cursor and Cline, and rank apps by tokens and spend. #### Up-to-Date Model List Refresh provider model lists hourly with admin approval. #### Proactive Alerts Get notified when providers are down or error rates climb. #### Self-Hosted Runs as one Go application with SQLite and an included Docker image. #### Built-In AI Workspace Chat directly with models, with persistent conversations and live budget insight. #### Skills & Plugins Personalize chats with reusable skills and connect external tools. #### Administration & Onboarding Manage keys, models, and users with a guided first-run setup. ### Product Tour See current spend, money left, and recent requests in one view.Filter requests by result, model, and date.Find the apps that use the most tokens, requests, and money.Track one app's usage, spend, errors, speed, and models over time.Check the delivery type, token use, cost, context, and saved content for one request.Give teams one list of model names, context sizes, and prices. ### Unified API & Every Provider Use one base URL and key format across all connected providers, with the model names your team already knows. #### Single Endpoint One base URL and key format across all connected providers. #### OpenAI-Compatible Works with clients that support the OpenAI API format. #### Chat, Text, Embeddings & Responses Supports chat, text generation, embeddings, and the OpenAI Responses API. #### Live Streaming Supports regular responses and live streaming. #### Stable Model Names Keeps public model names stable even when provider model names change. #### Familiar Errors Returns errors in the same basic format as OpenAI. #### Cloud & Private Providers OpenRouter, OpenAI-compatible services, private vLLM servers, and Gemini on Google Distributed Cloud. #### Custom CA Certificates Custom CA certificates for private providers. #### Loop Protection Loop protection when one laroute gateway connects to another. ### Keys, Budgets & Team Sharing Create virtual keys for people and apps, keep provider keys hidden, and give teams shared accounts with their own budgets. #### Virtual Keys Separate virtual keys for people and apps. #### Hidden Provider Keys Provider keys stay hidden from users. #### Key Expiry Add expiry dates to keys. #### Spending Limits Set daily, weekly, or monthly spending limits. #### HTTP 402 on Exhaustion Stop new requests when the budget is used up. #### Self-Service Keys Users manage their own keys through an optional API. #### Shared App Accounts Team accounts with their own keys and budgets. #### Per-App History Keep a request history for each app. #### Member Visibility Members see the apps they belong to. ### Observe Every Request & Usage Inspect every call, detect the agents consuming tokens, and keep the model list current. #### Request Details Model, key, result, token use, cost, and response time. #### Saved Previews Open a request to view request and response previews. #### Filters Filter requests by result, model, and date. #### Streamed Detection See whether a response was streamed. #### Tool Detection Detects Cursor, Cline, Aider, OpenWebUI, and common SDKs. #### App Rankings Rank apps by tokens, requests, or spend. #### Errors & Speed Error rates and average response times. #### Model Catalogue Central searchable model list with pricing and context info, synced hourly with admin approval. #### Company-Wide View Switch between personal and company-wide usage. ### Experience the AI Workspace Chat directly with models with persistent conversations, tools, and live budget insight.Request Demo ### A Built-In AI Workspace Use laroute directly as an AI chat workspace, not just an API gateway, with persistent conversations and live budget insight. #### Native Chat Workspace Chat with models directly, alongside the API gateway. #### Model & Reasoning Control Pick models and adjust reasoning effort right from the composer. #### Live Usage Indicators See context usage and budget status while chatting. #### Model Price & Context View pricing and context limits before choosing a model. #### Persistent & Resumable Replies Replies keep streaming through refresh and resume when you return. #### Long-Chat Compaction Compact long conversations to continue past the context limit. #### Reply Regeneration Regenerate answers and keep alternate versions. #### Tool Activity & Call Cards See when the assistant uses a tool and what it returned. #### Markdown Tables & Video Render model tables cleanly and attach videos to conversations. ### Manage, Administer & Self-Host Alerts, MCP, skills, plugins, administration, notifications, retention, and a self-hosted runtime on your own systems. #### Proactive Alerts Provider downtime alertsError-rate threshold alertsBudget-level alerts #### MCP Management Connect through the MCP endpoint at /mcpOAuth sign-in from clients like ChatGPTManage accounts, keys, providers, models, and alertsInteractive dashboards in MCP Apps #### Employee Portal SSO sign-inSpend, money left, and next budget resetSearch and filter request logsMasked API keysProfile page and light/dark/system themes #### Self-Hosted Runtime Included Docker imageHealth check at GET /api/healthOptional OpenTelemetry metricsProvider keys, settings, and logs stay on the laroute server #### Skills Reusable personal skillsCreate and manage skills from the Customize areaImport a skill from MarkdownStart a skill with a chat prompt #### Plugins & Connectors Personal and public workspace pluginsGuided connector setup with health checksOAuth connector sign-inEmbedded connector interfacesConnector failure isolation #### Administration & Onboarding Administration panel with search across collectionsRequest traffic separation and filteringKey, provider, and model managementGuided first-run setup and admin onboarding tour #### Notifications Notification centerPer-user notification preferencesAdmin alert subscriptionsSignup and key-change notices #### Privacy & Retention Configurable prompt and response retentionKeep usage history without keeping content foreverClear retention status #### Licensing Licence status in administrationLicence expiry warningsDegraded or inactive licence notices ### Get Started with Laroute Deploy on your own infrastructure: one OpenAI-compatible gateway for every AI model your team uses, with full sovereignty.Deploy Laroute #### --- # https://www.kalvad.com/products/ LarouteSelf-Hosted AI GatewayOne OpenAI-compatible API for cloud and private models, with a built-in AI workspaceDiscover LarouteJinnAutomated Server Security & Vulnerability Patching PlatformTrusted by 500+ enterprises across the gulf region & beyondDiscover JinnHakeemAl-Powered Feedback AnalysisGain instant, detailed insights from your feedback data.Discover HakeemHadirSmart Attendance & Workforce ManagementAccurate attendance tracking with real-time location validationDiscover Hadir #### --- # https://www.kalvad.com/services/automation/ ### Problems We Solve Automation projects fail from over-engineering and poor maintenance, not from lack of tools. Below are the situations we encounter most often.Manual deployments that break under pressureSomeone runs a script from their laptop, forgets a step, and production is down. We replace manual deployments with automated pipelines: build, test, deploy, verify. Every deployment is repeatable, auditable, and reversible. No more tribal knowledge or deployment checklists.CI/CD pipelines that are slow and fragileTests take 45 minutes. Builds fail randomly. Nobody trusts the pipeline so they deploy manually anyway. We optimize pipelines for speed and reliability: parallel test execution, caching, flaky test isolation, and clear failure messages. A pipeline should give fast feedback, not slow the team down.Infrastructure drift from manual changesA server is patched by hand, a config file is edited directly, and the environment no longer matches the code that describes it. We implement infrastructure as code so every change is version-controlled, reviewed, and reproducible. Manual changes are for debugging, not operations.Testing bottlenecks that delay releasesTests are an afterthought, run manually before release, or skipped entirely when deadlines loom. We integrate automated testing into the pipeline: unit tests on every commit, integration tests on every merge, and end-to-end tests on every deploy. Testing is part of the process, not a gate at the end. ### What We Deliver Concrete automation services, not vague promises.CI/CD pipeline engineeringBuild, test, and deploy pipelines that are fast, reliable, and observable. From simple workflows to complex multi-environment orchestration.Infrastructure as codeTerraform, Ansible, and custom tooling. Every server, network, and configuration defined in code and version-controlled.Automated testing & QAUnit, integration, and end-to-end tests integrated into the pipeline. Tests that catch regressions, not slow down development.Deployment orchestrationBlue-green, canary, and rolling deployments. Automated rollbacks and health checks. Deployments that are boring, not exciting. ### Our Approach Automation is not about replacing people, it is about removing friction. We start by understanding your current workflow, identifying the highest-friction processes, and automating incrementally. Every pipeline is version-controlled, every script is tested, and every automation has a clear path to recovery when things go wrong.Pipeline audit & assessmentArchitecture designIncremental automationProduction rolloutMonitoring & alertingDocumentation & runbooks ### Why Work With Kalvad? Engineering-first, not tool-firstDeep experience with CI/CD, IaC, and deployment automationTool-agnostic: we choose what fits your workflowIncremental automation, not big-bang rewritesLong-term maintainability over clever scriptsOpen-source first: we build on and contribute to open-source toolsWant to understand our engineering philosophy? Learn more about why we do things differently. ### Related Blog Posts Why We Chose Woodpecker CI (And You Probably Should Too)While everyone argues about GitHub Actions and GitLab CI, we quietly run Woodpecker CI. Here is why it became our default for self-hosted pipelin...Compliance Standards Are Not Carved in Stone, Stop Treating Them Like They Are: A 2026 Field ReportYour compliance standards are not carved in stone, your competitors are not waiting for your next change board meeting, and your security team ha...Security is a never-ending process, not a validation step! (2026 edition)Four years later, half of what I wrote is embarrassing. The other half is still true. Your WAF is still useless, your SIEM still spams your on-ca...From Mic Blip to Wiki Page: Meet Hubert, Our Self-Writing Meeting MachineMeet Hubert, our self-writing meeting machine, inspired by the legendary OSS 117. Like Hubert Bonisseur de la Bath, it navigates chaos with effor... ### Related Product JinnAutomated Server Security & Vulnerability Patching PlatformTrusted by 500+ enterprises across the gulf region & beyondDiscover Jinn ### Related Guide On-Premise ProductionLearn why leading organizations are bringing infrastructure back in-house: cost control, data sovereignty, and operational excellence without vendor lock-in. Real-world patterns from production deployments.Read on-premise guide ### Frequently Asked Questions Which CI/CD tool should we use?+It depends on your hosting preference, team size, and existing tooling. GitHub Actions and GitLab CI are popular and well-supported. Woodpecker CI is lightweight and self-hostable. Jenkins is flexible but requires more maintenance. We evaluate your requirements and recommend the tool that fits your workflow, not the one that is most trendy. We also migrate between tools when the current one is holding your team back.Should we automate everything at once?+No. Automation should be incremental. We start with the highest-impact, highest-friction processes: deployments, testing, and environment provisioning. Once those are stable, we expand to areas like database migrations, certificate management, and incident response. Automating too much too quickly creates brittle pipelines that are harder to maintain than the manual processes they replaced.How do you handle rollbacks when automated deployments fail?+Every automated deployment includes a rollback strategy. This can be as simple as reverting to the previous container image, or as complex as running a full DAG execution including restoring backups, reverting database schema, and leveraging PITR. We test rollback procedures as part of the pipeline, not just the forward deploy. The goal is that when something goes wrong, the system recovers automatically or with a single command, not a frantic debugging session.How long does it take to set up a CI/CD pipeline?+A basic pipeline with build, test, and deploy stages can be set up in a few days. A comprehensive pipeline with multiple environments, approval gates, security scanning, and performance testing typically takes 2 to 4 weeks. The timeline depends on the complexity of your application, the number of environments, and the level of automation you want to achieve.What happens after the automation is in place?+We document everything: pipeline architecture, configuration, and runbooks for common issues. We can stay on for ongoing maintenance and optimization, or hand over to your team with training sessions. The pipelines we build are designed to be understandable and maintainable by your engineers, not dependent on us.Let's talk about your automation strategyWhether you are building CI/CD pipelines, implementing infrastructure as code, or trying to eliminate manual bottlenecks in your workflow, we can help. No sales pitch: just an engineering conversation about your processes, your constraints, and what comes next.Talk with our engineers #### --- # https://www.kalvad.com/services/cloud-services/ ### Problems We Solve Cloud projects rarely fail from lack of tools. They fail from lack of planning. Below are the situations we encounter most often.Cloud bills that grow without explanationThe first month is affordable. Six months later, the bill has tripled and nobody knows why. Unused instances, orphaned storage, data transfer charges, and over-provisioned resources add up silently. We implement cost visibility from day one: tagging, budgeting, alerts, and regular audits so you know exactly what you are paying for.Migration projects that stall halfwayLift-and-shift moves the problem to the cloud without solving it. Applications run slower, costs spike, and the team is stuck managing two environments. We plan migrations workload by workload: assess dependencies, design cloud-native architectures where it makes sense, migrate in phases, and validate at each step before moving on.Vendor lock-in with no exit strategyProprietary services are convenient until you need to migrate, negotiate pricing, or handle an outage in a specific region. We design for portability: containerized workloads, infrastructure as code, and abstraction layers around vendor-specific APIs. You should own your architecture, not rent it from a single provider.Performance that degrades at cloud scaleWhat worked with three services becomes a bottleneck at thirty. Latency between availability zones, database connection limits, and network throughput caps appear without warning. We design for scale from the start: proper service boundaries, caching strategies, connection pooling, and load testing before production traffic arrives. ### What We Deliver Concrete cloud services, not vague promises.Cloud architecture & designVPCs, subnets, security groups, and service mesh. Cloud infrastructure designed for security, performance, and cost efficiency.Migration & modernizationLift-and-shift when appropriate, refactor when it matters. Phased migrations with rollback plans at every stage.Cost optimization & FinOpsRight-sizing, reserved capacity, spot instances, and continuous monitoring. Cloud spend that makes financial sense.Managed cloud operationsOngoing management, patching, scaling, and incident response. Your cloud running smoothly without a dedicated ops team. ### Our Approach Cloud migration is not a destination, it is a transition. We start by understanding your current systems, workload characteristics, and business constraints. Every migration is phased, every architecture is version-controlled, and every decision is documented so your team understands not just what was built, but why.Workload assessmentPhased migrationInfrastructure as codeFinOps practicesCloud-native patternsDocumentation & runbooks ### Why Work With Kalvad? Engineering-first, not sales-firstPlatform-agnostic: AWS, Azure, GCP, or hybridHonest about cloud trade-offs, not a cloud evangelistInfrastructure as code and open standardsCost optimization built into every architectureLong-term maintainability over quick migrationsWant to understand our engineering philosophy? Learn more about why we do things differently. ### Related Blog Posts Security is a never-ending process, not a validation step! (2026 edition)Four years later, half of what I wrote is embarrassing. The other half is still true. Your WAF is still useless, your SIEM still spams your on-ca...DevOps in 2025: The Pauperization Machine (Now With More AI and Less Brain Cells!)In 2021, concerns were about Kubernetes and sysadmin basics. By 2025, DevOps is characterized by AI over-reliance and waning technical depth. Kal...Live Free or RAM Hard: Declarative Linux StyleTired of bricking Linux servers? Try Alpine Linux: a lightweight, secure NixOS alternative. Explore diskless and data modes for better security a...DNS Load Balancing: The Illusion of SimplicityExplore when to use DNS load balancing and the key conditions for its effectiveness in managing network traffic.... ### Related Product LarouteSelf-Hosted AI GatewayOne OpenAI-compatible API for cloud and private models, with a built-in AI workspaceDiscover Laroute ### Frequently Asked Questions Should we migrate to the cloud or stay on-premise?+It depends on your workload characteristics, compliance requirements, and cost structure. Cloud is excellent for variable workloads, rapid scaling, and reducing capital expenditure. On-premise makes sense for predictable high-volume workloads, strict data residency requirements, or when cloud egress costs outweigh the benefits. We help you evaluate the trade-offs honestly. Some workloads belong in the cloud, some on-premise, and many work best in a hybrid setup.How long does a cloud migration take?+A focused migration of a single application can take 4 to 8 weeks. A full organizational migration with dozens of services typically spans 6 to 18 months. We prioritize workloads by business impact and technical complexity, migrating in phases so you see progress early. Each phase includes testing, validation, and rollback procedures so nothing is left to chance.How do you control cloud costs?+Cost optimization starts with architecture: right-sizing instances, using reserved or spot capacity where appropriate, and designing for efficient resource usage. We implement tagging, budgeting, and alerting so you always know what is being spent and why. We also review architectures quarterly to catch cost drift before it becomes a problem. The goal is not to minimize cost at the expense of reliability, but to eliminate waste while maintaining performance.Do you recommend multi-cloud or single-cloud?+Multi-cloud adds significant complexity: different APIs, different tooling, different operational models. We only recommend it when there is a clear business case: regulatory requirements, geographic coverage, or avoiding single-vendor dependency for critical workloads. For most organizations, a well-architected single-cloud setup with portable infrastructure as code provides the best balance of capability and operational simplicity.What happens after migration is complete?+We offer ongoing cloud management and optimization, but we also design systems to be operable by your team. Every migration includes documentation, runbooks, monitoring dashboards, and knowledge transfer sessions. We can stay on for continuous optimization, hand over operations gradually, or provide periodic reviews depending on your needs.Let's talk about your cloud architectureWhether you are planning a migration, trying to control cloud costs, or dealing with performance issues in an existing cloud setup, we can help. No sales pitch: just an engineering conversation about your workloads, your constraints, and what comes next.Talk with our engineers #### --- # https://www.kalvad.com/services/custom-software-development/ ### Problems We Solve Every project starts with a problem, not a feature list. Below are the situations we encounter most often.Legacy systems that slow down every changeMonolithic codebases with no tests, tangled dependencies, and tribal knowledge. We untangle incrementally: extracting bounded contexts, adding test coverage around critical paths, and introducing boundaries that make future changes predictable.Off-the-shelf software that does not fitA platform that does 60 percent of what you need, badly, and forces workarounds. Custom software starts from your actual processes and builds around them. Fewer workarounds, more time on work that matters.Scaling pain: the system works until it does not100 users fine. 10,000 and it falls apart: slow queries, race conditions, timeouts. We find the real bottleneck: database locks, N+1 queries, synchronous blocking calls, or architecture that assumed single-tenant.Integration chaos: no single source of truthCRM, ERP, billing, and internal tools each hold a piece of the data puzzle. We design integration layers that consolidate data flows, handle failures gracefully, and replace point-to-point spaghetti with event-driven patterns. ### Typical Projects Examples of what we actually build.Internal business platformsCRMs, ERPs, and operational tools that match how your team actually works, not how a vendor imagined it.Customer portalsSelf-service platforms for your clients: order tracking, account management, and support workflows.Operational dashboardsReal-time visibility into your systems, KPIs, and business metrics with actionable data.API integrationsConnecting your systems to external services, payment providers, logistics, and data sources. ### Our Approach Every project starts by understanding the business problem, operational constraints, and long-term maintenance requirements. We favour incremental delivery, production-ready architectures, and transparent engineering decisions over large upfront specifications.Incremental deliveryMaintainable architecturesTesting from day oneProduction-ready deploymentsTechnology chosen for the problem, not trendsKnowledge transfer ### How We Work A clear process from first conversation to production and beyond.1DiscoveryUnderstand the problem, constraints, and success criteria.↓→2ArchitectureDesign the system, choose the stack, plan the milestones.↓→3DevelopmentBuild in increments with working software at each step.↓→4ProductionDeploy, monitor, and iterate based on real usage.↓→5SupportMaintain, optimize, and hand over to your team when ready. ### Why Work With Kalvad? Engineering-first, not sales-firstModern, maintainable technology stacksProduction experience across AI, infrastructure and softwarePragmatic architectures instead of unnecessary complexityLong-term partnership, not just project deliveryOpen-source first: we build on and contribute to open-source toolsWant to understand our engineering philosophy? Learn more about why we do things differently. ### Related Blog Posts Django Doesn't Have to Feel Old: Modernize Your Frontend with ViteStill Using Plain CSS and JS in Django? Modernize with Vite I've heard this complaint from Django developers for years, and I've made it myself ...RAG Deep Dive Series: RAG ArchitecturesPart 7: RAG Architectures - From Basic to Big Brain Mode...How To Boost Your Angular Application PerformancePerformance can make or break an Angular app. Top tips to speed up and optimize your app....Goodbye Zone.js ? Angular’s Zone-less Change Detection ExplainedZone-less Angular actually kicked off back in version 17, and since then, they’ve been polishing it bit by bit. Now, with Angular 20, turning off... ### Related Product LarouteSelf-Hosted AI GatewayOne OpenAI-compatible API for cloud and private models, with a built-in AI workspaceDiscover Laroute ### Frequently Asked Questions How long does a custom software project take?+It depends on scope and complexity. A focused internal tool might ship in 6 to 8 weeks. A full enterprise platform with multiple integrations, user roles, and data pipelines typically runs 3 to 6 months. We break projects into deliverable increments so you see working software early and can adjust priorities as we go.Do you work with existing codebases?+Yes. A significant portion of our work involves extending, refactoring, or modernizing existing systems. We start with an audit of the current architecture, identify bottlenecks and technical debt, and propose a plan that balances quick wins with long-term structural improvements. We do not recommend full rewrites unless the existing system is beyond salvage.What technologies do you use?+We choose technologies based on the problem, not trends. Our experience spans TypeScript, Angular, React, Python, Java, Elixir, and Gleam for application development. For data layers we use PostgreSQL, MySQL, TigerData, and SQLite. The stack is always a deliberate choice aligned with your team's capabilities and the system's long-term maintenance requirements.How do you handle testing and quality assurance?+Testing is part of the development process, not an afterthought. We write unit tests for business logic, integration tests for API boundaries, and end-to-end tests for critical user flows. Performance and load testing are included for systems that need to handle real traffic. Every pull request requires review before merge, and CI pipelines enforce quality gates automatically.What happens after the software is delivered?+We offer ongoing support and maintenance contracts, but we also design systems to be maintainable by your own team. Documentation, clean architecture, and readable code are non-negotiable. We can train your engineers, hand over operations gradually, or continue as a long-term partner depending on what works best for your organization.Let's talk about your systemWhether you are building from scratch, modernizing a legacy codebase, or scaling a system that is starting to show its age, we can help. No sales pitch: just an engineering conversation about your architecture, your constraints, and what comes next.Talk with our engineers #### --- # https://www.kalvad.com/services/cybersecurity/ ### Problems We Solve Security failures are rarely about lack of tools. They are about lack of preparation. Below are the situations we encounter most often.Vulnerabilities discovered too lateA security researcher finds your exposed database. A competitor finds your leaked API key. An attacker finds your unpatched server. We identify vulnerabilities before they are exploited: penetration testing, vulnerability scanning, and security assessments that find real problems, not just theoretical risks.Known vulnerabilities that never get fixedScan results that sit unread. Patches that are delayed because nobody owns the process. Critical CVEs that linger for months while the risk compounds. We implement vulnerability management workflows that turn scan results into action: prioritized remediation, automated patch tracking, and accountability that closes the loop.Incident response without a planSomething is wrong, but nobody knows who should investigate, who should communicate, or what to do first. Hours are lost while the attacker moves laterally. We help you prepare before an incident: define roles, establish procedures, set up detection, and practice response so your team knows exactly what to do when something happens.Security tools that create noise, not signalHundreds of alerts per day, most of them false positives. A SIEM that nobody looks at. Vulnerability scanners that report thousands of issues with no prioritization. We implement security monitoring that is actionable: alerts that mean something, dashboards that show real risk, and processes that turn noise into decisions. ### What We Deliver Concrete security services, not vague promises.Penetration testingControlled assessments of your systems. Web apps, APIs, infrastructure, and networks. Real vulnerabilities with proof of concept and remediation steps.Security hardeningServer hardening, network segmentation, access control, and secure configurations. Systems designed to resist attacks from day one.Incident responsePreparation, detection, containment, and recovery. Response plans that work when seconds matter, not just on paper.Vulnerability managementContinuous scanning, patch tracking, and remediation workflows. Security that is ongoing, not just point-in-time assessments. ### Our Approach Security is not a product, it is a process. We start by understanding your systems, your data, and your threat model. Every assessment is thorough, every recommendation is actionable, and every defense is designed for the reality of your environment, not a textbook scenario. We prepare you for incidents before they happen, not after.Threat modelingVulnerability assessmentDefense implementationMonitoring & detectionIncident responseDocumentation & runbooks ### Why Work With Kalvad? Engineering-first, not compliance-checkbox-firstDeep experience with Linux, cloud, and application securityPractical defenses, not theoretical frameworksIncident response preparation before incidents happenLong-term security posture over quick fixesOpen-source security tools: we use what works, not what is expensiveWant to understand our engineering philosophy? Learn more about why we do things differently. ### Related Blog Posts The AI War Has Begun. And You're Already Conscripted.On June 13, 2026, the US government blocked Fable 5, firing the first shot in the AI War. At Tech Fest Nancy the day before, François Zaninotto's...Compliance Standards Are Not Carved in Stone, Stop Treating Them Like They Are: A 2026 Field ReportYour compliance standards are not carved in stone, your competitors are not waiting for your next change board meeting, and your security team ha...Security is a never-ending process, not a validation step! (2026 edition)Four years later, half of what I wrote is embarrassing. The other half is still true. Your WAF is still useless, your SIEM still spams your on-ca...Introduction to Quantum CryptographyQuantum cryptography is a new method of cryptography that it’s based on quantum mechanics laws, pretending that uncertainty has the potency to we... ### Related Product JinnAutomated Server Security & Vulnerability Patching PlatformTrusted by 500+ enterprises across the gulf region & beyondDiscover Jinn ### Related Guide On-Premise ProductionLearn why leading organizations are bringing infrastructure back in-house: cost control, data sovereignty, and operational excellence without vendor lock-in. Real-world patterns from production deployments.Read on-premise guide ### Frequently Asked Questions What does a penetration test involve?+A penetration test is a controlled assessment of your systems to identify vulnerabilities that an attacker could exploit. We start by defining the scope: which systems, what level of access, and what rules of engagement. Then we systematically probe for weaknesses: misconfigurations, outdated software, weak authentication, injection flaws, and more. The output is a detailed report with findings ranked by severity, proof of concept for critical issues, and specific remediation steps. We do not just find problems, we help you fix them.How often should we do security assessments?+At minimum, annually. But the real answer depends on your change velocity. If you deploy weekly, add new services regularly, or handle sensitive data, quarterly assessments make more sense. Any major change, infrastructure migration, or new compliance requirement should trigger a fresh assessment. Security is not a one-time activity, it is a continuous process. We help you establish a cadence that matches your risk profile and operational rhythm.How do you handle vulnerability management?+Vulnerability management is not just running a scanner. We implement a full workflow: regular scanning of your infrastructure and applications, prioritization based on actual exploitability and business impact, coordinated patching with minimal downtime, and verification that fixes actually work. We track vulnerabilities from discovery to resolution, so nothing slips through the cracks.What happens if we get breached?+If you have an incident response plan in place, the damage is contained faster and recovery is quicker. We help you prepare before an incident: define roles, establish communication channels, set up monitoring that catches breaches early, and practice response procedures. When an incident occurs, we follow a structured process: contain, investigate, eradicate, recover, and learn. The goal is not just to fix the immediate problem but to make the system more resilient.Do you provide ongoing security management?+Yes. Security is not a project with an end date. We offer ongoing security management: vulnerability scanning, patch management, log monitoring, incident response readiness, and periodic assessments. We can act as your security team, augment your existing capabilities, or provide periodic reviews. The level of engagement depends on your needs, risk tolerance, and internal resources.Let's talk about your security postureWhether you are managing vulnerabilities, responding to a security incident, or building proactive defenses, we can help. No sales pitch: just an engineering conversation about your systems, your risks, and what comes next.Talk with our engineers #### --- # https://www.kalvad.com/services/data-ai/ ### Problems We Solve Data and AI projects fail from unrealistic expectations and poor engineering, not from lack of tools. Below are the situations we encounter most often.Data silos with no single source of truthCRM, ERP, spreadsheets, and SaaS tools each hold a piece of the picture. Nobody knows which numbers are correct. We design data pipelines that consolidate and transform data so your team works from a single, reliable source.AI demos that never reach productionThe prototype works great with a curated dataset. Production traffic reveals LLM latency issues, hallucination problems, and cost overruns. We engineer AI systems for production from day one: evaluation pipelines, caching, fallback strategies, and monitoring so the system works when real users depend on it.Analytics dashboards nobody trustsReports that show different numbers depending on who runs them. Metrics defined differently across teams. Data that is hours or days old by the time it appears. We build analytics systems with clear data lineage, consistent metric definitions, and automated refresh cycles so people actually use the dashboards.Data pipelines that break silentlyA schema changes upstream, a connector fails, and nobody notices until a report is wrong or a model produces garbage output. We implement data quality checks, alerting, and observability so pipeline failures are caught and resolved before they impact decisions. ### What We Deliver Concrete data and AI services, not vague promises.Data pipeline engineeringIngestion, transformation, and storage pipelines. Batch and streaming. Data that flows reliably from source to insight.Analytics & BI systemsDashboards, reports, and self-service analytics. Metrics that are consistent, current, and actually used by decision-makers.AI/ML production systemsMachine learning and LLM systems deployed with evaluation, monitoring, and fallback strategies. Systems that work beyond the demo.RAG architecture & deploymentRAG systems that connect your data to LLMs with proper chunking, embedding, evaluation, and latency optimization. Production RAG, not prototypes. ### Our Approach Every data and AI project starts with understanding what data exists, what decisions need to be made, and what constraints shape the architecture. We favour iterative delivery with working systems at each stage, production-grade engineering from day one, and transparent decisions about trade-offs between accuracy, latency, and cost.Data audit & assessmentArchitecture designIterative deliveryProduction deploymentMonitoring & evaluationDocumentation & knowledge transfer ### Why Work With Kalvad? Engineering-first, not hype-firstProduction experience with RAG, LLM, ML, and analytics at scaleHonest about what AI can and cannot do todayData quality and observability built into every pipelineLong-term maintainability over quick demosOpen-source first: we build on and contribute to open-source toolsWant to understand our engineering philosophy? Learn more about why we do things differently. ### Related Blog Posts The AI War Has Begun. And You're Already Conscripted.On June 13, 2026, the US government blocked Fable 5, firing the first shot in the AI War. At Tech Fest Nancy the day before, François Zaninotto's...From Mic Blip to Wiki Page: Meet Hubert, Our Self-Writing Meeting MachineMeet Hubert, our self-writing meeting machine, inspired by the legendary OSS 117. Like Hubert Bonisseur de la Bath, it navigates chaos with effor...RAG Deep Dive Series: Evaluation & ProductionPart 8: How to Know If Your RAG System Is Actually Good (And Keep It That Way)...RAG Deep Dive Series: RAG ArchitecturesPart 7: RAG Architectures - From Basic to Big Brain Mode... ### Related Product HakeemAl-Powered Feedback AnalysisGain instant, detailed insights from your feedback data.Discover Hakeem ### Related Guide RAG Architecture in ProductionA practical guide to building production-grade RAG systems. Covers architecture, components, evaluation, latency optimization, and real-world trade-offs.Read RAG guide ### Frequently Asked Questions Can we put AI into production, or is it still experimental?+AI systems are in production today, but they require different engineering practices than traditional software. LLMs are non-deterministic, latency-sensitive, and expensive to run at scale. We design AI systems with proper evaluation pipelines, fallback strategies, caching layers, and monitoring so they behave predictably in production. The technology is mature enough for production use, but it needs to be engineered correctly.Is our data ready for analytics and AI?+Most organizations have more data than they realize, but it is scattered across databases, spreadsheets, and SaaS tools. We start with a data audit: what exists, where it lives, what shape it is in, and what gaps matter for your use case. We then design ingestion pipelines, transformation layers, and storage strategies that make the data accessible and reliable. You do not need perfect data to start, you need a plan to make it usable.Should we use RAG, fine-tuning, or both?+It depends on your use case. RAG is ideal when you need the system to reference current, specific, or proprietary information that the base model was not trained on. Fine-tuning works better when you need the model to adopt a specific style, format, or domain expertise. In practice, we often combine both: a fine-tuned model for behavior and a RAG layer for knowledge retrieval. We evaluate your requirements and recommend the approach that balances accuracy, cost, and maintainability.How long does it take to build a production AI system?+A focused RAG prototype with a defined knowledge base can be built in 4 to 6 weeks. A full production system with evaluation pipelines, monitoring, fallback strategies, and integration into existing workflows typically takes 3 to 6 months. We deliver working increments early so you can validate the approach before committing to a full build. The timeline depends on data availability, integration complexity, and the level of accuracy required.What happens after the system is deployed?+AI systems require ongoing monitoring and tuning. Model performance drifts, data sources change, and user expectations evolve. We set up evaluation pipelines, usage dashboards, and alerting so you know when the system is underperforming. We can provide ongoing optimization, retraining, and architecture reviews, or train your team to manage the system independently. Documentation and knowledge transfer are included in every engagement.Let's talk about your data architectureWhether you are building data pipelines, deploying LLM systems, or trying to make sense of existing data, we can help. No sales pitch: just an engineering conversation about your data, your goals, and what comes next.Talk with our engineers #### --- # https://www.kalvad.com/services/infrastructure-services/ ### Problems We Solve Infrastructure issues rarely announce themselves until something breaks. Below are the situations we encounter most often.Systems that go down without warningNo monitoring, no alerts, no visibility. You only know something is wrong when users start complaining. We implement observability from the ground up: metrics, logs, traces, and alerts that catch issues before they become outages.Scaling bottlenecks that appear without warningThe system handles 100 users fine, but at 1,000 it starts timing out. Database connections exhaust, disk I/O saturates, or network bandwidth caps out. We identify capacity limits, implement load balancing, and design for horizontal scaling before you hit the wall.Vendor lock-in and runaway cloud costsManaged services are convenient until the bill arrives. Proprietary APIs make migration impossible. We design infrastructure that is portable: open standards, infrastructure as code, and clear exit strategies. You own your setup, not the vendor.Manual processes that break under pressureDeployments done by hand, backups that are never tested, configurations that drift over time. We automate everything: CI/CD pipelines, configuration management, backup verification, and disaster recovery drills. Manual work is for debugging, not operations. ### What We Deliver Concrete infrastructure services, not vague promises.Network architectureVLANs, firewalls, load balancers, DNS, and routing. Networks designed for security and performance, not just connectivity.Server management & hardeningLinux and FreeBSD systems configured for production: minimal attack surface, automated updates, and secure defaults.Storage & backup strategiesRAID, ZFS, distributed storage, and backup pipelines. Data that is actually recoverable, not just copied.Monitoring & observabilityMetrics, logs, traces, and alerts. Dashboards that show you what matters, not just what is easy to collect. ### Our Approach Infrastructure is not a set-and-forget activity. We design systems with observability, automation, and maintainability as first-class concerns. Every configuration is version-controlled, every change is tested, and every system has a clear path to recovery.Infrastructure as codeObservability by designAutomated recoveryCapacity planningSecurity-first configurationsDocumentation & runbooks ### Why Work With Kalvad? Engineering-first, not sales-firstDeep experience with Linux and FreeBSD in productionPlatform-agnostic: cloud, on-premise, or hybridOpen standards and infrastructure as codeLong-term maintainability over quick fixesTransparent communication and documentationWant to understand our engineering philosophy? Learn more about why we do things differently. ### Related Blog Posts Why We Chose Woodpecker CI (And You Probably Should Too)While everyone argues about GitHub Actions and GitLab CI, we quietly run Woodpecker CI. Here is why it became our default for self-hosted pipelin...Compliance Standards Are Not Carved in Stone, Stop Treating Them Like They Are: A 2026 Field ReportYour compliance standards are not carved in stone, your competitors are not waiting for your next change board meeting, and your security team ha...Security is a never-ending process, not a validation step! (2026 edition)Four years later, half of what I wrote is embarrassing. The other half is still true. Your WAF is still useless, your SIEM still spams your on-ca...RAG Deep Dive Series: Evaluation & ProductionPart 8: How to Know If Your RAG System Is Actually Good (And Keep It That Way)... ### Related Product JinnAutomated Server Security & Vulnerability Patching PlatformTrusted by 500+ enterprises across the gulf region & beyondDiscover Jinn ### Related Guide On-Premise ProductionLearn why leading organizations are bringing infrastructure back in-house: cost control, data sovereignty, and operational excellence without vendor lock-in. Real-world patterns from production deployments.Read on-premise guide ### Frequently Asked Questions Do you manage infrastructure remotely?+Yes. Most of our infrastructure work is done remotely. We connect via secure channels, audit existing systems, implement changes, and set up monitoring so you have visibility into what is happening. For on-premise deployments, we work with your local team or provide detailed runbooks and documentation for handover.Should we go cloud or on-premise?+It depends on your workload, compliance requirements, and cost structure. Cloud offers elasticity and managed services but can become expensive at scale. On-premise or self-hosted gives you full control and predictable costs but requires more operational discipline. We help you evaluate the trade-offs and design a setup that matches your actual needs, not vendor recommendations. We also handle hybrid setups where specific workloads live on-premise and others in the cloud.How do you handle emergencies and outages?+We set up monitoring and alerting so we know about issues before your users do. When an incident occurs, we follow a clear process: triage, mitigate, communicate, and resolve. Post-incident, we conduct a blameless review to understand root causes and implement preventive measures. The goal is not just to fix the immediate problem but to make the system more resilient over time.Do you work with existing infrastructure or only greenfield?+Both. A significant portion of our work involves auditing and improving existing setups: identifying bottlenecks, hardening security, adding observability, and automating manual processes. We also design and deploy greenfield infrastructure from scratch. In both cases, we document everything and ensure the system is maintainable by your team.What monitoring and observability tools do you use?+We choose tools based on the environment and requirements. Our experience includes Prometheus, Grafana, OpenObserve, OpenTelemetry, and custom logging pipelines. We focus on actionable metrics: not just collecting data, but setting up alerts that tell you when something actually needs attention. We also implement distributed tracing for complex systems where understanding request flows is critical.Let's talk about your infrastructureWhether you are dealing with reliability issues, planning a migration, or building from scratch, we can help. No sales pitch: just an engineering conversation about your systems, your constraints, and what comes next.Talk with our engineers #### --- # https://www.kalvad.com/services/ ## Our Services Kalvad provides data analytics & AI, cybersecurity, custom software development, infrastructure, automation, and cloud services. ### Data Analytics & AI Turn raw data into production-ready intelligence. We design and deploy analytics and AI systems, combining machine learning, retrieval-augmented generation (RAG), and modern data pipelines to transform large, complex datasets into insights you can actually use. From surfacing hidden patterns to enabling AI-powered decision support, our engineers focus on systems that work beyond the demo: reliable, explainable, and built to operate at scale.Related insights:RAG Deep Dive Series: From Decision to ProductionRead moreRAG Deep Dive Series: Evaluation & ProductionRead moreLearn more ### Cybersecurity Protect what matters. We identify vulnerabilities, harden your systems, and implement proactive defenses against evolving threats, from penetration testing to incident response and compliance audits. Security isn't an afterthought; it's built into everything we do. Our experts help you safeguard sensitive data, maintain compliance, and preserve customer trust.Related insights:Security is a never-ending process, not a validation step! (2026 edition)Read moreCheap DDoS on-demand or How to properly load test your application!Read moreLearn more ### Custom Software Development We build software tailored to your business needs. From mobile apps to enterprise platforms, our developers deliver scalable, high-performance solutions using modern stacks and proven methodologies. Every project starts with understanding your goals. We work alongside your team to design systems that solve real problems and streamline operations.Related insights:Django Doesn't Have to Feel Old: Modernize Your Frontend with ViteRead moreHow To Boost Your Angular Application PerformanceRead moreLearn more ### Infrastructure Services Reliable infrastructure is the backbone of every successful operation. We design, deploy, and manage IT systems optimized for performance, security, and scale, from network architecture to server management and storage solutions. Our engineers implement battle-tested configurations that keep your systems running smoothly, so you can focus on your business.Related insights:The 5 Stages of Monitoring: From 'Firefighting' to 'Fire Prevention'Read moreOur Journey Through Linux/Unix LandscapesRead moreLearn more ### Automation Ship faster. Break less. Our DevOps and automation services streamline your entire software lifecycle, CI/CD pipelines, infrastructure as code, automated testing, and deployment orchestration. We eliminate manual bottlenecks so your team can focus on building features, not fighting fires. The result: shorter cycles, higher quality, faster time-to-market.Related insights:Breaking Up with GitHub Actions: Our Love Story with Woodpecker CIRead moreIt is Infrastructure as Code, not Infrastructure as YAML!Read moreLearn more ### Cloud Services Migrate to the cloud with confidence. We architect, deploy, and manage cloud infrastructure across AWS, Azure, and GCP, helping you unlock flexibility, reduce costs, and scale on demand. Whether you're starting fresh or modernizing legacy systems, we handle the complexity so you can focus on outcomes, not infrastructure.Related insights:DNS Load Balancing: The Illusion of SimplicityRead moreUptime Kuma, SQLite and Litestream!Read moreLearn more #### --- # https://www.kalvad.com/why/ ### Why Organizations Work With Us Engineering-First ApproachWe solve engineering problems before proposing technologies. Every architecture, framework and deployment strategy is selected because it fits the project's requirements, not because it follows the latest trend.Production ExperienceWe design systems that continue to operate reliably long after launch. Observability, maintainability and operational simplicity are considered from the first day of every project.Long-Term PartnershipsMany of our engagements extend beyond the initial delivery. We help organizations evolve their platforms over time through continuous improvements, technical guidance and ongoing support.Open Source MindsetWe actively contribute to open-source software, publish engineering guides and share practical knowledge with the wider developer community. Our internal standards are influenced by the same engineering principles we apply to public projects.Experience in Complex EnvironmentsFrom government entities to private organizations, we have delivered systems where security, scalability and reliability are essential rather than optional.Transparent CollaborationWe work as an extension of your engineering team. Technical decisions, trade-offs and project progress remain visible throughout the entire engagement. ### Engineering Experience We have delivered data and software platforms for public institutions, enterprises and fast-growing organizations where reliability and correctness were non-negotiable.Government PlatformsEnhancing Service ExcellenceAn initiative to improve public services in the UAE using a star-based rating system, including independent audits and customer surveys.TechnologiesPython, Django, Angular, PostgreSQL, Dramatiq, LavinMQ, KubernetesEngineering highlightHandles 85 million hits per day using less than 4 cores and 4GB of RAM.Government PlatformsListening to CitizensA platform allowing users to rate government services and provide feedback via surveys.TechnologiesPython, Django, Angular, Django Ninja, PostgreSQL, TimescaleDB, LavinMQ, Celery, KamalEngineering highlight1.6TB of compressed data with 99th percentile response time of 0.9 seconds.Government PlatformsEvaluating Government PerformanceProject to evaluate key performance indicators of government entities, ensuring effectiveness and transparency.TechnologiesJava, SpringBoot, Angular, Python, RabbitMQ, MySQL, PyInfraEngineering highlightDatabase bandwidth of 120 MBps.Government PlatformsStreamlining Project ManagementA tool for high-ranking UAE officials to track projects, targets, and timelines, promoting efficiency.TechnologiesPython, Dart, Flutter, PostgreSQL, Redis, KubernetesEngineering highlightDelivered 1 week ahead of schedule and maintains 10 releases per week.Government PlatformsFeeding the NeedyA project distributing meals to underserved communities, aligning with UAE's humanitarian goals.TechnologiesZola, Cloudflare, Elixir, PostgreSQLEngineering highlightSupported up to 18 million concurrent users, though payment gateway had load issues.Enterprise SystemsRevolutionizing Hospitality ExperienceA digital platform enhancing hotel guest experiences through mobile ordering and services integration.TechnologiesPython, Django, PostgreSQL, Elixir, RabbitMQ, n8n, Clever CloudEngineering highlightIntegrated with over 20 payment gateways and major PoS systems.AI & DataEnhancing Opinion AnalysisUtilizes Python, Ollama, and various advanced models to perform efficient and scalable opinion analysis on text data. By integrating local large language models through Ollama, the project ensures data privacy and control.TechnologiesPython, Django, PostgreSQL, OllamaEngineering highlightLocal-first LLM deployment eliminates API dependencies while enabling deep customization for privacy-sensitive analysis.85MRequests per daySingle government platform handling 85 million daily requests using less than 4 cores and 4GB of RAM.1.6TBCompressed datasetsCitizen feedback platform with 1.6TB of compressed data and sub-second 99th percentile response times.18MConcurrent usersHumanitarian platform supporting up to 18 million concurrent users during peak demand.120MB/sDatabase bandwidthGovernment performance evaluation system sustaining 120 MBps of continuous database throughput. ### How We Work DiscoveryUnderstand business objectives, technical constraints and existing systems before proposing solutions.ArchitectureDesign maintainable, scalable and production-ready architectures aligned with long-term goals.ImplementationDevelop software incrementally with code reviews, testing and continuous collaboration throughout the project.DeploymentDeliver reliable production environments with automation, monitoring and operational best practices.Continuous ImprovementSupport, optimize and evolve systems as business requirements change over time.Testing & Quality AssuranceAutomated testing at every level: unit, integration and end-to-end. Quality is verified continuously, not checked at the end. ### Our Philosophy Good engineering is invisible when it works but costly when it doesn't. We believe the best systems are those you can trust without thinking about them.This is why we obsess over details, test assumptions, and design for failure before it happens. ### Technical Expertise Custom Software DevelopmentModern web applications, APIs and business platforms engineered for maintainability and long-term evolution.Infrastructure ServicesReliable Linux infrastructure, Kubernetes, networking, observability and production operations.Cloud ServicesPublic, private and hybrid cloud environments designed around operational requirements rather than vendor preference.Data Analytics & AIProduction-ready analytics platforms, machine learning pipelines, LLM integrations and RAG architectures.AutomationCI/CD, Infrastructure as Code, deployment automation and engineering workflows that reduce manual effort.CybersecuritySecurity hardening, vulnerability management and practical protection strategies integrated throughout the software lifecycle. ### Proof Beyond Client Work Engineering GuidesWe publish practical guides on RAG architecture, on-premise production and other topics we encounter in real projects.Open SourceWe maintain and contribute to production-grade open-source projects, from Django patches to infrastructure tools.ProductsJinn, Hakeem and Hadir: production tools built by Kalvad and used in real environments.Conference TalksWe share our experience at international conferences, including FOSDEM and regional engineering events. ### Industries GovernmentSecure, compliant and highly available platforms supporting critical public services.EducationDigital platforms that remain reliable during periods of heavy usage while supporting evolving educational needs.HealthcareSystems where security, privacy and operational continuity are fundamental requirements.EnterpriseBusiness-critical applications that integrate with existing systems while supporting long-term growth.StartupsScalable architectures that enable rapid iteration without compromising future maintainability.Non-ProfitPlatforms that maximize impact with limited resources, where reliability and cost-efficiency are essential. ### Frequently Asked Questions Do you only work with large organizations?+No. We work with startups, SMEs, enterprises and public-sector organizations. Every engagement is adapted to the project's complexity and objectives.Can you modernize existing software?+Yes. Many of our projects involve modernizing legacy applications, improving architectures and introducing new technologies without disrupting existing operations.Do you provide long-term support?+Yes. We frequently continue working with clients after deployment through maintenance, improvements and technical guidance.Can you integrate with our existing systems?+Yes. We regularly build solutions that integrate with existing software, APIs and infrastructure rather than replacing them entirely.How do projects usually begin?+Most engagements start with a technical discussion to understand objectives, constraints and existing systems before defining the appropriate architecture and implementation approach.Do you only build AI solutions?+No. While AI is one of our areas of expertise, we also design custom software, infrastructure, automation and cloud solutions. We recommend AI only when it provides genuine value for the project. ### Discuss your project with our engineers Start with a technical conversation about your objectives, constraints and possible approaches.Get in touch #### --- # https://www.kalvad.com/ KALVAD IN NUMBERS200+Projects delivered100+Clients worldwide30+In-House experts6+Years in business ### What We Do Engineering services designed to solve complex, real-world problems.Talk to our expertsCustom SoftwareWeb applications, APIs and business platforms engineered for maintainability.InfrastructureLinux, networking and production operations.CloudPublic, private and hybrid cloud environments designed around operational requirements.Data & AIAnalytics platforms, machine learning pipelines and LLM integrations.AutomationCI/CD, Infrastructure as Code and deployment automation.CybersecuritySecurity hardening, vulnerability management and compliance frameworks. ### Our Clients Proudly serving government entities, enterprises, and innovative organizations across the region.+100 other valued clients ### Engineering Guides In-depth technical guides on building production-grade systems.RAG Architecture in ProductionA practical guide to building production-grade RAG systems. Covers architecture, components, evaluation, latency optimization, and real-world trade-offs.Read RAG guideOn-Premise ProductionLearn why leading organizations are bringing infrastructure back in-house: cost control, data sovereignty, and operational excellence without vendor lock-in. Real-world patterns from production deployments.Read on-premise guide ### Discover Our Products Explore our cutting-edge solutions.LarouteSelf-Hosted AI GatewayOne OpenAI-compatible API for cloud and private models, with a built-in AI workspaceDiscover LarouteJinnAutomated Server Security & Vulnerability Patching PlatformTrusted by 500+ enterprises across the gulf region & beyondDiscover JinnHakeemAl-Powered Feedback AnalysisGain instant, detailed insights from your feedback data.Discover HakeemHadirSmart Attendance & Workforce ManagementAccurate attendance tracking with real-time location validationDiscover Hadir ### Our Latest Blog Posts Insights from Kalvad on engineering, product updates, and technical deep-dives.Django Doesn't Have to Feel Old: Modernize Your Frontend with ViteStill Using Plain CSS and JS in Django? Modernize with Vite I've heard this complaint from Django developers for years, and I've made it myself ...Why We Chose Woodpecker CI (And You Probably Should Too)While everyone argues about GitHub Actions and GitLab CI, we quietly run Woodpecker CI. Here is why it became our default for self-hosted pipelin...The AI War Has Begun. And You're Already Conscripted.On June 13, 2026, the US government blocked Fable 5, firing the first shot in the AI War. At Tech Fest Nancy the day before, François Zaninotto's...Compliance Standards Are Not Carved in Stone, Stop Treating Them Like They Are: A 2026 Field ReportYour compliance standards are not carved in stone, your competitors are not waiting for your next change board meeting, and your security team ha... ### Case Studies & Events Explore our involvement, innovations, and insights.Kalvad Migration Success with QuickwitRead about Kalvad's seamless migration to Quickwit's search engine solution and enhanced performance....Cybersecurity Seminar: Become a Cybersecurity ExpertKalvad hosted a seminar emphasizing the importance of mastering development skills to excel in cybersecurity....Kalvad's Success with SenXDiscover how Kalvad leveraged SenX solutions to enhance data analysis and system efficiency....Kalvad at the CUD Career FairKalvad proudly participated in the Canadian University Dubai's annual career fair, inspiring students in their career journeys.... ### Built by Engineers, for Engineers At Kalvad, we solve complex business challenges with technology that works. Our team combines deep technical expertise with analytical precision to deliver systems that perform. By understanding your unique goals, we design and implement custom software systems tailored precisely to your requirements.About us ### Driven by Excellence, No Compromise Our vision is simple: excelsior. We never deliver subpar work. Every project meets the highest standards of quality, innovation, and reliability, consistently exceeding client expectations and industry benchmarks. We pair this commitment with a pragmatic, engineering-first mindset. Practical solutions to complex problems. This blend of high standards and execution is why clients trust Kalvad to deliver outstanding results.Why Kalvad ####