A deployed product, not a demo
Real users can sign in and use it. Frontend, backend and LLM layer ship together.
Services
What we build.
Four kinds of AI work, from a first product to features inside software you already run. We also design and build the websites, dashboards and backends these systems live in.
AI Products & MVPs
We take an AI product idea from prototype to a working, deployed product: frontend, backend, LLM layer and the evaluation that keeps it honest.
- LLM apps
- MVPs
- Next.js + FastAPI
Evaluation built in
Scores are computed in code and the LLM cites real data, so quality is measured, not guessed.
A first version scoped in writing
We agree what the first release does and what done means before we write code.
Code and docs you own
We hand over the codebase and documentation, and stay available to plan the next version.
Typical stack
- Next.js · Reactproduct frontend
- FastAPI · Node.jsbackend and APIs
- LangChain · LangGraphLLM layer
- Supabase · PostgreSQLdata and auth
- Vercel · Render · AWSdeployment
AI Agents
Agents that plan, call your tools and APIs, and hand off to a person when they should, with guardrails, fallbacks and logs you can audit.
- Tool calling
- LangGraph
- Guardrails
- Human-in-the-loop
An agent with one real job
We start from a workflow your team runs today and define what done looks like, so the agent has a job, not a vague brief.
Tool calls you control
The agent reaches your CRM, docs and APIs through defined tools with clear permissions, rate limits and timeouts.
Guardrails and fallbacks
PII checks, grounding checks and provider fallback ship in the first deploy. When a check fails, the agent stops or hands off.
An audit trail for every run
Each plan, tool call, check and decision is logged, so you can see why the agent did what it did.
Typical stack
- LangGraphagent orchestration and state
- LangChainmodel and tool adapters
- FastAPItool endpoints and services
- Node.jsintegrations and webhooks
- Supabase · PostgreSQLdata, logs and audit trail
AI Automation
Workflows that read, classify, summarise and route emails, documents, leads and support tickets, so repetitive work stops landing on your team.
- Document & email flows
- Integrations
- Human-in-the-loop
Inboxes that sort themselves
Incoming emails, documents and tickets are read, classified and summarised as they arrive.
Work sent to the right place
Items are routed to the right person or system through integrations with the tools you already use.
People kept in the loop
Review checkpoints where judgement matters, with corrections fed back into the flow.
Logs you can audit
Every classification and routing decision is recorded.
Typical stack
- LangChain · LangGraphclassification and summarisation
- FastAPI · Node.jsintegrations and webhooks
- Supabase · PostgreSQLqueue, records and logs
- Vercel · Render · AWSdeployment
AI Features in Your App
We add search, assistants, summaries, speech and vision to your existing product without rebuilding what already works.
- RAG & search
- Assistants
- Speech & vision
Features that fit your product
Search, assistants, summaries, speech or vision built into your existing codebase and UI.
Answers grounded in your data
Retrieval over your own documents, with citations users can check.
No rewrite
We work inside what already works and add only what the feature needs.
Production safeguards
Guardrails, provider fallback, rate limits and cost controls from the first deploy.
Typical stack
- LangChain · LangGraphretrieval and generation
- Whisper · MediaPipespeech and vision
- FastAPI · Node.jsfeature APIs
- Supabase · PostgreSQLdata and search
- Next.js · Reactin-app UI
FAQ
Choosing the right build.
Not sure which of the four fits? These usually settle it.
Use normal automation when the steps are fixed and predictable. Use an agent when the steps depend on messy input like emails, documents or open-ended requests, and the system has to decide which action fits. Many good systems combine both.
Yes. We integrate RAG search, assistants, summarisation, speech and computer vision into apps that already run, working with your existing codebase and without rebuilding what already works.
We limit which tools it can call, validate every tool input and output, add human approval before high-stakes actions, and log every step. Agents should fail safely and hand off to a person, not guess.
We agree the scope and timeline in writing before we start, then show you working software every week, so you see progress early.
Every project is scoped individually. After a discovery call we send a fixed-scope proposal with a clear price, timeline and deliverables. No hourly rates or template pricing.
Ready to start?
Tell us what you want to build.
A few lines are enough. We reply within 24 hours with questions or a first outline of scope and approach.