AI Development

A typical first release ships in 2-6 weeks; a full AI MVP in 6-8. We start with a thin, deployable slice instead of a long discovery phase, and AI clears the repetitive code so senior engineers focus on architecture and evals. You get a clickable staging URL from week one.

We build on GPT, Claude, Gemini, and open-weight models, picked per use case on eval scores, latency, and cost. Training a model from scratch is almost never worth it. Fine-tuning sometimes is; we propose it only when evals show a clear gain. Model calls sit behind one abstraction so you can switch later.

You do, fully. Code, prompts, eval suites, and data land in your repository and your accounts as we build, with documentation and a handover runbook. There is no proprietary platform and no lock-in. Your data is processed under a DPA and is never used to train anything.

With evals. Before launch we build a test set from real inputs, define accuracy thresholds with you, and run the suite on every change in CI. You see the scores, not a demo that worked once. Nothing ships below the threshold we agreed on, and the eval suite stays in your repo.

Your system keeps working. Every model call goes through one abstraction layer, so a deprecated model is a one-line swap, not a rewrite. We rerun the eval suite against the new model, compare scores, and ship the change behind a reviewed pull request. Maintenance plans cover this as routine work.

Yes; most engagements start exactly that way. A proof of concept covers one core flow, runs on your data, and deploys to a URL you can click, usually within two weeks. It starts around $2,000. If it earns a full build, the code carries forward; nothing gets thrown away.

Yes. Every build includes deployment, a written runbook, and two weeks of post-launch support. After that you choose: take it fully in-house, keep us on a light retainer for model updates and eval reruns, or scope the next phase. The code is yours either way, so the choice stays real.