Most AI features break because the model is asked to do work that should be plain code. I build them the other way round: a tested, deterministic core, and a language model only where language is the actual job. My production example turns a pricing matrix and a short brief into a branded Word proposal, with 170 automated tests around it.

Teams that draft proposals, reports or replies by hand every day

Support teams that want answers grounded in their own documents

Products that need an assistant, search or classification

Telegram and Discord bots that should reply with AI

Scoping. We decide what the model does and what stays in code, so numbers and rules never come from a guess.

Integration. Claude or OpenAI APIs from Python or Node, with structured output and validation.

Your data. Retrieval over your documents where it helps, with sources shown in the answer.

Tests and evals. Automated tests for the code and a set of real cases to check the model's output against.

Cost and limits. Caching, rate limits and a clear monthly cost estimate.

Handover. The source code, prompts, test cases and a short guide.

You send the idea, what it should do and the deadline

I reply the same day with questions or a plan and a fixed price

You follow progress on a live test version, with updates every few days

I deploy, hand everything over and fix anything that comes up in the first two weeks

Training models from scratch. I build on top of existing models.

Everything happens in writing, no calls needed. Send the task, the deadline and the budget, and I reply the same day.