# TechKis

> TechKis is an AI-first software & engineering studio. We build end-to-end platforms and implement AI in your workflows — from LLM agents and RAG assistants to the full-stack web, mobile, desktop and backend services needed to ship them. Actively booking our first 2–3 founding clients.

TechKis is an AI-first software engineering studio. We build end-to-end platforms and implement AI in your workflows — full-stack web apps, mobile apps, desktop apps and backend services, with LLM agents, RAG assistants and workflow automation baked in where they help.

- Website: https://techkis.tech
- Blog: https://blogs.techkis.tech
- Email: techkis.tech@gmail.com
- Location: Remote · Global
- LinkedIn: https://www.linkedin.com/company/tech-kis/
- Instagram: https://www.instagram.com/techkis.tech/
- Status: actively booking our first 3 founding clients
- Pricing: tailored to scope on a 30-minute discovery call — written proposal within 48 hours

## Our work
- [Puratan Ayurveda](https://techkis.tech/work/puratan-ayurveda) (Founding Client #1) — Puratan Ayurveda needed a home of their own online — a store that matched the brand, handled real orders, and didn't lock them into a monthly SaaS bill.

## Services
- [AI Agents & Workflows](https://techkis.tech/services/ai-agents-workflows): LLM agents that take real action — read your docs, call your APIs, ship work back.
- [RAG & Knowledge Assistants](https://techkis.tech/services/rag-knowledge-assistants): Internal copilots that actually know your company — docs, wiki, Slack, code, with citations.
- [Workflow Automation](https://techkis.tech/services/workflow-automation): Replace manual ops work with AI-augmented pipelines — n8n, Temporal or pure code.
- [AI Strategy & Architecture](https://techkis.tech/services/ai-strategy-architecture): Find the AI & automation opportunities that actually move your business — then a roadmap to execute.
- [Full Stack Web Development](https://techkis.tech/services/full-stack-web-development): Customer-facing platforms end-to-end — Next.js, React 19 and TypeScript front to back.
- [Mobile App Development](https://techkis.tech/services/mobile-app-development): iOS and Android apps — React Native or native — with AI features that actually feel native.
- [Desktop App Development](https://techkis.tech/services/desktop-application-development): Cross-platform desktop apps — Electron or Tauri — with the polish of a native client.
- [API Platforms](https://techkis.tech/services/api-platforms): Versioned, documented, well-tested HTTP and event APIs your customers and downstream teams will enjoy.
- [Cloud & AWS / DevOps](https://techkis.tech/services/cloud-aws-devops): Cloud-native delivery on AWS — IaC, CI/CD, observability and a sensible monthly bill.

## Use cases we automate
- [Customer support](https://techkis.tech/#use-cases): Triage inboxes, draft on-brand replies, route tickets with full context — and escalate only what truly needs a human.
- [Internal knowledge](https://techkis.tech/#use-cases): A company-wide copilot that answers from your docs, wiki, Notion and Slack — with citations, access control and audit trails.
- [Document & data extraction](https://techkis.tech/#use-cases): Pull structured data from PDFs, contracts, invoices and reports straight into your systems. No more manual entry.
- [Sales & outreach](https://techkis.tech/#use-cases): Enrich leads, draft personalised outreach and keep your CRM clean — automatically, with humans approving the sends.
- [Content operations](https://techkis.tech/#use-cases): Repurpose long-form into briefs, social posts and newsletters with brand-aware tone, fact checks and a review queue.
- [Developer workflows](https://techkis.tech/#use-cases): PR review summaries, ticket triage, changelog generation and intelligent on-call digests — built around your existing tools.

## Industries we serve
- [Fintech & Financial Services](https://techkis.tech/industries): Compliance-aware platforms for payments, lending and wealth.
- [SaaS & Developer Tools](https://techkis.tech/industries): Multi-tenant platforms, billing and developer experience.
- [Healthcare & MedTech](https://techkis.tech/industries): HIPAA-aware workflows for clinical and patient data.
- [Retail & E-commerce](https://techkis.tech/industries): Headless commerce, AI search and personalisation.
- [EdTech & Learning](https://techkis.tech/industries): AI tutors, content generation and LMS integrations.
- [Travel & Hospitality](https://techkis.tech/industries): Booking flows, dynamic pricing and AI trip planners.
- [Media & Marketing](https://techkis.tech/industries): Content ops, attribution and AI-augmented creative.
- [Logistics & Operations](https://techkis.tech/industries): Document processing, dispatch and workflow automation.

## Capabilities
- [AI & ML Engineering](https://techkis.tech/capabilities#ai-ml): LLM agents & tool-use, RAG pipelines, Vector search, Evals & benchmarks, Fine-tuning & adapters, Multimodal, Prompt engineering, Cost & latency tuning
- [Backend Engineering](https://techkis.tech/capabilities#backend): JVM services, Node & TypeScript APIs, Python services, Microservices & DDD, Event-driven systems, REST & GraphQL APIs, Relational databases, Caching & queues, Observability
- [Frontend, Mobile & Desktop](https://techkis.tech/capabilities#frontend-mobile): Modern web frameworks, TypeScript front-to-back, Design systems & theming, Streaming AI UIs, Cross-platform mobile, Native iOS & Android, Cross-platform desktop, Accessibility
- [Cloud & DevOps](https://techkis.tech/capabilities#cloud-devops): Cloud platforms, Infrastructure as code, CI/CD pipelines, Containers & orchestration, Compliance readiness, Cost optimization, Secrets & identity, GPU autoscaling
- [Product & Architecture](https://techkis.tech/capabilities#product-architecture): System design reviews, Migration & modernization, Performance engineering, Security audits, Technical due diligence, ADRs, Design systems, Documentation

## Stack we work in
- **AI & LLM**: OpenAI, Anthropic, LangChain, LangGraph, pgvector
- **Automation**: n8n, Temporal, Playwright, Make.com, Lambdas / Cron
- **Backend & Data**: Java, Spring Boot, Node.js, PostgreSQL, Redis
- **Frontend & Cloud**: Next.js, React, TypeScript, AWS, Docker

## How we work
- **01 Discovery & AI fit** — A focused 30-minute call to map the AI / automation opportunities that actually move your business — and the ones that don't.
- **02 Proposal & eval plan** — Within 48 hours: written scope, milestones, deliverables, a fixed-or-time-boxed estimate and the evaluation criteria for every AI feature.
- **03 Weekly AI sprints** — Ship AI features behind flags every week — evaluated before they touch production users, with public changelog and decision logs.
- **04 Launch & guardrails** — Production launch with monitoring, evals, fallbacks and a 30-day support window — or an ongoing retainer for tuning and new features.

## FAQ
- [Will AI workflows actually work reliably in production?](https://techkis.tech/#faq): Yes, with the right approach: tight scope, evals before launch, human-in-the-loop on anything irreversible, fallbacks for model failures, and observability so you see drift before customers do. That's the bar we hold ourselves to.
- [What does TechKis do?](https://techkis.tech/#faq): We build end-to-end platforms and implement AI in your workflows.
- [How do you price projects?](https://techkis.tech/#faq): We tailor pricing to scope on a 30-minute discovery call rather than publishing rate cards online. Send us the brief and we'll come back with a written, fixed-or-time-boxed proposal within 48 hours — including a founder-friendly offer for our first 2–3 founding clients.
- [Is TechKis a new company?](https://techkis.tech/#faq): Yes — we're a brand-new studio actively looking for our first founding clients. Our founders are senior engineers who've been building with LLMs since GPT-3, with prior experience at product startups. We're offering founder-friendly terms on our first 2–3 engagements while we build our portfolio — happy to discuss details on a scoping call.
- [Do you have client case studies yet?](https://techkis.tech/#faq): Yes — our first founding client is Puratan Ayurveda, an Ayurvedic e-commerce brand we took from marketplace-only to a fully owned online store. We're actively booking our next 1–2 founding clients.
- [How does an engagement start?](https://techkis.tech/#faq): We hop on a 30-minute scoping call, then send a written proposal with scope, milestones, deliverables, evaluation plan and a fixed or time-boxed estimate within 48 hours.
- [Do you work with existing in-house teams?](https://techkis.tech/#faq): Absolutely. We plug into your Git, Slack, Linear/Jira and CI workflows, pair with your engineers, and try to leave them more capable than we found them — especially on AI tooling and evals.
- [What is the typical project timeline?](https://techkis.tech/#faq): Most engagements run 4–12 weeks. We work in weekly sprints with a public changelog so you always know where things stand.
- [Do you offer ongoing support after launch?](https://techkis.tech/#faq): Yes — flat-rate monthly retainers for maintenance, eval monitoring, model upgrades and incremental feature work once the initial delivery is live.

## Other pages
- [About](https://techkis.tech/about)
- [Industries](https://techkis.tech/industries)
- [Capabilities](https://techkis.tech/capabilities)
- [AI Services deep dive](https://techkis.tech/capabilities/ai-services)
- [Custom Software Development deep dive](https://techkis.tech/capabilities/custom-software-development)
- [Experience Transformation deep dive](https://techkis.tech/capabilities/experience-transformation)
- [Founding Partner Program](https://techkis.tech/capabilities/founding-partner-program)
- [Privacy policy](https://techkis.tech/privacy)
- [Terms of service](https://techkis.tech/terms)
- [Blog (Hashnode)](https://blogs.techkis.tech)
- [Insights / on-site](https://techkis.tech/insights)
  - [A Lighthouse 100 doesn't mean your site is fast](https://techkis.tech/insights/lighthouse-score-vs-core-web-vitals): Lighthouse is a lab simulation on a throttled mid-range phone. Google ranks on field CrUX p75 for LCP, INP and CLS. Here's why the two disagree, and what to measure instead.
  - [Fixing Core Web Vitals in the Next.js App Router](https://techkis.tech/insights/fixing-core-web-vitals-nextjs-app-router): A hands-on guide to fixing LCP, INP and CLS in the Next.js App Router — client boundaries, next/image, next/font, Suspense streaming and a Lighthouse CI budget.
  - [Getting cited by AI search — how to be the answer, not the tenth blue link](https://techkis.tech/insights/getting-cited-by-ai-search): ChatGPT, Perplexity and Google AI Overviews cite sources instead of listing links. Here's how answer engines pick what to quote, and how to be the citation.
  - [MCP vs A2A vs HTTP APIs — what's actually different, and when each one wins](https://techkis.tech/insights/mcp-vs-a2a-vs-http-apis): MCP, A2A and plain HTTP APIs solve overlapping but distinct problems. Here's what each protocol is really for, how they compose, and how to pick without cargo-culting the newest acronym.
  - [Why every SaaS will have AI agents by 2027 — and what that changes about how you build](https://techkis.tech/insights/why-every-saas-will-have-ai-agents): Agents are moving from bolt-on chat widgets to the primary way users get work done inside software. Here's the shift that's driving it, what an agent-native SaaS actually looks like, and what it means for your architecture and moat.
  - [Building AI features without LangChain — the raw-SDK path most teams should take first](https://techkis.tech/insights/building-ai-features-without-langchain): You don't need a framework to ship an AI feature. Here's what the raw provider SDK actually gives you, the small amount of glue you write yourself, and when a framework finally earns its place.
  - [RAG is not dead — you're just building it wrong](https://techkis.tech/insights/rag-is-not-dead): "Just use a bigger context window" doesn't kill RAG. Bad chunking, no reranking, and retrieval you never evaluate kill RAG. Here's what a retrieval pipeline that actually works looks like.
  - [AI memory explained — short-term vs long-term, and why the difference matters](https://techkis.tech/insights/ai-memory-short-term-vs-long-term): "Memory" in AI apps means two very different mechanisms with different costs and failure modes. Here's what short-term (context) and long-term (retrieval/state) memory actually are, and how to design both.
  - [Prompt engineering is becoming a software engineering problem](https://techkis.tech/insights/prompt-engineering-software-engineering): Clever wording was phase one. Production prompts now need versioning, tests, evals, regression tracking and CI — the same discipline as any other code. Here's what that looks like in practice.
  - [How AI changes backend architecture — the parts that are genuinely different](https://techkis.tech/insights/how-ai-changes-backend-architecture): Adding an LLM to your backend breaks assumptions that held for a decade: latency, determinism, cost per request, statefulness. Here's what actually changes and how to design for it.
  - [What AI coding agents actually change about shipping client software](https://techkis.tech/insights/ai-coding-agents-on-client-work): AI coding agents make generation cheap and review expensive. What actually gets faster on client work, what doesn't, and verification discipline that keeps it safe.
  - [The AI gateway pattern for enterprise applications](https://techkis.tech/insights/ai-gateway-pattern-enterprise): Letting every service call model providers directly is how enterprises lose control of cost, security and compliance. An AI gateway is the single seam where routing, auth, budgets, logging and guardrails live. Here's how to build one.
  - [AI observability — logs, traces, cost and hallucinations](https://techkis.tech/insights/ai-observability-logs-traces-cost): You can't run an LLM system on hope. Traditional observability misses the things that actually go wrong with AI: silent quality drift, runaway token cost, and confident wrong answers. Here's the observability stack an AI system needs.
  - [AI rate limiting and cost control — before the bill surprises you](https://techkis.tech/insights/ai-rate-limiting-and-cost-control): One buggy loop or abusive user can turn an LLM feature into a five-figure invoice overnight. Rate limiting and cost control for AI aren't the same as for a normal API — here's how to cap spend without breaking the product.
  - [Scaling from 10 users to 10 million — the decisions that matter at each stage](https://techkis.tech/insights/scaling-from-10-to-10-million-users): Scaling isn't one problem. It's a sequence of different problems that appear at different stages. Here's what actually matters at 10 users, 10,000 users, 100,000 users, and 10 million — and the mistakes that come from solving tomorrow's problem today.
  - [API-First Development — why the contract should come before the code](https://techkis.tech/insights/api-first-development): API-First means designing the contract before writing the implementation. Here's why that order matters, what it changes about how teams work, and the practical workflow that makes it work without slowing you down.
  - [Domain-Driven Design for startups — the parts worth keeping, the parts worth skipping](https://techkis.tech/insights/domain-driven-design-for-startups): DDD is a powerful set of ideas buried under a lot of enterprise ceremony. Here's what's genuinely useful for a startup — bounded contexts, ubiquitous language, aggregates — and what you can safely skip until you need it.
  - [Hexagonal Architecture without overengineering — ports, adapters and the line you shouldn't cross](https://techkis.tech/insights/hexagonal-architecture-without-overengineering): Hexagonal Architecture is one of the most useful ideas in software design. It's also one of the most over-applied. Here's what the ports-and-adapters model actually buys you, where it becomes ceremony, and how to apply it without drowning in abstractions.
  - [Clean Architecture in real projects — what survives contact with a deadline](https://techkis.tech/insights/clean-architecture-in-real-projects): Clean Architecture looks elegant on a diagram. In production it gets messy. Here's what the layers actually buy you, where the theory breaks down, and the pragmatic version we apply on real client work.
  - [Monolith vs Modular Monolith vs Microservices — picking the right shape for your stage](https://techkis.tech/insights/monolith-vs-modular-monolith-vs-microservices): A practical breakdown of when a monolith is the right call, when to modularise it, and when microservices actually earn their operational cost — with the signals that tell you it's time to move.
  - [LangGraph vs LangChain vs raw OpenAI SDK in 2026 — what we actually pick](https://techkis.tech/insights/langgraph-vs-langchain-vs-openai-sdk-2026): An opinionated, code-first comparison of LangGraph, LangChain and the raw OpenAI / Anthropic SDKs for shipping production agents in 2026 — when each one earns its keep, where they bite, and what we default to on new client work.
  - [pgvector vs Pinecone vs Qdrant at 10M chunks — what we'd actually run](https://techkis.tech/insights/pgvector-vs-pinecone-vs-qdrant-at-10m-chunks): A practical comparison of pgvector, Pinecone and Qdrant for RAG at the 10-million-chunk threshold — where Postgres still wins, where managed beats self-hosted, and the real cost trade-offs that actually decide it.
  - [Tauri vs Electron in 2026 — binary size, RAM, auto-update and the parts no one benchmarks](https://techkis.tech/insights/tauri-vs-electron-binary-ram-autoupdate): A practical comparison of Tauri and Electron for desktop apps in 2026 — measured binary size and RAM, the real auto-update stories, code-signing pain, and where each one earns its keep.
  - [Google I/O 2026 — what stood out to our team](https://techkis.tech/insights/google-io-2026-highlights): Our take on the Google I/O 2026 announcements that actually matter for shipping AI-first software in production — Gemini, Android, web platform and developer tooling.
  - [React Native vs Expo vs Flutter for AI apps in 2026](https://techkis.tech/insights/react-native-vs-expo-vs-flutter-for-ai-apps): An opinionated comparison of React Native, Expo and Flutter through the lens of shipping AI-first mobile apps — streaming UIs, on-device inference, native bridges and the team-velocity trade-offs that actually decide it.
  - [AWS Bedrock vs direct provider APIs — cost, lock-in and the procurement reality](https://techkis.tech/insights/aws-bedrock-vs-direct-provider-apis): When to call OpenAI and Anthropic directly versus going through AWS Bedrock — a practical breakdown of cost, latency, model availability, governance and the procurement reality that actually decides the answer on most enterprise projects.

## Optional
- [Sitemap](https://techkis.tech/sitemap.xml)
- [Full content for LLMs](https://techkis.tech/llms-full.txt)
- [Contact](https://techkis.tech/#contact)