What we build
for other companies.

What it is

An AI feature is a product decision before it is a model decision: what it may read, what it may do, what it costs, and what happens when it is unsure.

We design that first, then build the retrieval, the prompting and the evaluation around it. The model is the easiest part to swap; the rest is what you keep.

How we build it

Retrieval over your own content, a prompt that is tested like code, an evaluation set that grows with every bug, and a cost meter from day one.

The feature ships inside your product with its budget, its log and its escalation rules, and we stay until your team can change the prompt, the sources and the limits without calling us.

What you get

A feature shipped inside your product with its retrieval, evaluation set, cost meter and escalation rules. The prompt and the sources are editable by your team, and the repository, architecture notes and runbook come with it.

What it is

Most of the software a company needs is not novel. It needs to be correct, fast, secure and boring to operate for years.

We build it in a small, well-known stack: TypeScript and Next.js on the front, Python or Node services behind, PostgreSQL underneath. We write it as if someone else will maintain it, because someone will.

How we build it

The API contract comes first and everything else is generated from it: the reference, the client library and the validation rules for every form. One source of truth, checked in CI.

Every repository ships with its architecture notes, its decisions recorded, and a deploy script that works from a clean machine.

What you get

The repository with the API contract, the generated reference and client, the test suite in CI, a deploy that works from a clean machine, and the architecture notes with every decision recorded.

What it is

Every growing team has a layer of copy-and-paste between systems that only exists because those systems do not talk to each other.

We map it, measure it, and replace it with integrations and agents. Two-way sync where it matters, a nightly batch where it does not, and a human step wherever the cost of being wrong is high.

How we build it

Each automation logs every run, retries what is safe to retry, and routes the rest to a person with the context already gathered.

Nothing goes live until your team has tried to break it on real data. Then we watch it for thirty days.

What you get

Integrations and agents running in your accounts, a log of every run, a retry policy, and one channel where exceptions arrive with their context, together with a map of the manual work we replaced and what it was costing you.

What it is

Before an AI can answer well it needs sources worth reading. That is a search and data problem long before it is a model problem.

We build the crawling, the extraction, the indexing and the ranking, for the open web, for a company's own documents, or for both. Underneath, we build the pipelines that keep a warehouse consistent and traceable.

How we build it

Discover, fetch, extract, rank, answer: one engine shape that fits the open web, an internal knowledge base or a product catalogue.

Pages are read in a real browser only when they must be, the text that matters is kept, and the answer lists where it came from. Underneath, the pipelines keep one definition of every metric and a link from every number to its row.

What you get

A crawl, extraction, indexing and ranking pipeline over your sources, answers that cite them, one definition of every metric, and the runbook to operate it.