From prompt to tool
AI built around a real job, with the data, checks, and workflow it needs to hold up.
“The prompt works. Only one person knows how to run it.”
We start from the decision or task the AI is supposed to help with, build the thinnest version that works end to end, then add what makes it dependable: the data pipeline, evaluation, a review step for the cases it gets wrong, and integration with the tools your team already uses.
Tarn: an AI tool we built and run, where the practitioner keeps the final say
What we do
- Define the job — What the system must do, and how you’ll know it’s doing it.
- End-to-end first — A thin working version before anything gets polished.
- Models and data — The right models, retrieval, APIs, and source data, connected.
- Evaluation and review — Uncertain or high-stakes cases go to a person.
- Production code — Documented, so your team can run it and improve it.
What usually goes wrong
- Treating a good model response as a finished product.
- Judging quality by how the demo felt instead of by a test you can rerun.
- AI that sits beside the workflow instead of inside it, so nobody uses it.
Which decision should AI make easier for your team?
Bring the use case. We’ll sketch the thinnest version that would prove it works.