AI MVP

While an AI project sits in slide decks, the market doesn't wait — a competitor ships something rough, learns from real users for six months, and the gap compounds. The cost of a stalled project isn't the budget, it's the position you don't get back.

A weekend prototype does the happy path. Then it meets real data, real volume and a real user who does something nobody predicted, and that gap is where projects stall for months.

We build one feature and put it in production. Retrieval over your documents, an agent that handles one workflow, a classifier that routes work — scoped in writing before anything starts. $4,500 buys a tightly scoped feature on clean data; $12,900 buys one with messy inputs, real integrations and an evaluation harness.

One feature ships; multi-agent systems and fine-tuning don't

The build runs in four fixed steps

1. Scope in writing, with the acceptance test named
2. The feature, on your data
3. An evaluation you can run yourself, not our word for it
4. Deployed, handed over, 30 days of support

The evaluation is the part most vendors skip

Anyone can show you a demo that works. We hand over a test you can run on your own data, so "it works" is a number rather than an impression. If it doesn't clear the bar we agreed, we fix it.

You own the code; we just pick where it runs

Your servers, ours, or Claude Cowork. You own the code and the IP. Models are picked per use case from Claude, Claude Cowork, Microsoft Copilot and the ChatGPT SDK, with speech and video where they fit.