Full-stack products with AI built in, on a data layer that holds up.
Alongside our data engineering and AI work, we build the applications people use: web products, internal tools and automations that put AI to work and make teams more productive.
- Next.js
- React
- TypeScript
- Node.js
- Python
- PostgreSQL
- AWS
- Vercel
- LLM APIs
- MCP
From a feature to a whole product.
Most of our work is data engineering and AI. When a client also needs the product on top, we build that too, end to end.
AI features in your product
Assistants, smart search, summarisation and drafting built into an app you already have, with the prompts, guardrails and evaluation that keep them reliable.
Internal tools on your data
Dashboards, review queues and admin consoles where people work alongside AI: the model drafts, a person approves, and every step is logged.
Workflow automation
Back-office processes such as triage, reconciliation and document handling, automated end to end with human checkpoints where judgement matters.
MVPs for new ideas
A focused first version of a new AI product, built to be shown to real users quickly and extended without a rewrite.
Product engineering with data discipline.
The same rules we apply to data platforms apply to the products on top of them.
- 01The data layer comes first: product features read from governed, tested data, never from ad-hoc exports
- 02Every AI feature ships with an evaluation set and a way to see what the model did
- 03People stay in control of decisions that matter, with approval steps in the product
- 04Costs are visible from day one: per-feature model spend, caching and sensible limits
- 05Plain, maintainable code your own team can take over, deployed through CI/CD
Built to run, not just to demo.
Products we build ship on an architecture that holds up in production:
- Serverless or containers, chosen by traffic shape and cost.
- CI/CD with preview environments and automated tests.
- Observability: logs, traces and alerts from the first release.
- Security: authentication, secrets management and least-privilege access.
- Costs that scale with usage, with model spend tracked per feature.
Experiments we have built.
Our Labs projects show how we approach full-stack AI products. They are experiments, not client work.
AI Gateway
One governed gate for every LLM call — models, MCP servers, guardrails, budgets, and audit logs in a single control plane.
Queryline
Plain language in, structured JSON out — one engine that plans, executes, and schema-validates across SQL, NoSQL, and the warehouse.
Knowledge Lens
Every data source in one knowledge base — chat on the left, dynamic views on the right that reshape to each question.
Have a product idea that needs AI?
Tell us who it is for and what it should do. We will tell you what the data needs to look like first.