Which model, how it finds the right answer in your data, and what it costs, decided with tests.
First callWeek 1
Milestone pricedWeeks 1-2
Every week there's a new model, and you need one decision that holds.
We test candidate models on your own examples and pick the best fit on quality, latency and cost, then keep the choice on the server so switching later is a config change.
You need to know what this costs per user before you promise it to anyone.
We estimate LLM running costs from real token counts before building, then cap them in production with per-user quotas.
Working buildsWeeks 2-7
The model sounds confident and is wrong about your business.
Retrieval over your own documents and data so answers come from what you actually know, with limits on what the model is allowed to read.
Reviewed and handed overWeek 8
Your AI feature can read data and call tools. Who checked what an attacker can make it do?
An LLM security review for startups: prompt injection, tool-call permissions, write access to your database, and cost attacks, with a written report and tests you keep.
We default to OpenAI's models and compare others for your case before deciding.
Yes. A security review covers prompt injection, tool permissions and cost attacks, and ends with written findings.
From real token counts on your own examples, per user and per feature, before anything is built.
15 or 30 minutes with the engineers who would write your code. If we're not the right fit, you hear it on that call.