Services
Services
Four kinds of production AI work, each described by the problem it solves. Start from the problem you have, not from a technology: find the line below that sounds like your week, and open it.
01 / Which one fits
If this sounds familiar, read this page
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Decision systems: You make the same costly decision every day, by rule of thumb
Simulation and reinforcement learning for repeated decisions, with an evaluation that compares any new policy against the rule you use today.
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Data pipelines: Your pipeline breaks at night, and someone fixes it by hand
Ingestion, validation and storage built to run unattended: checkpoints, retries, quality gates and a fallback when a source fails.
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ML applications: The model works. The system around it does not exist yet
The service, tests, deployment and monitoring around a working model, so it can be released, watched and rolled back without guesswork.
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LLM features: An LLM demo that nobody can measure
One feature built around one task, with an evaluation set written first, so every change to a prompt or a model is measured.
02 / The same in every service
What does not change from one service to the next
- Work starts with a fixed-price, fixed-scope pilot, agreed in writing. Its price and length are set in that scope.
- The code lives in your repository and runs in your accounts from the first commit.
- Every week there is something you can run yourself.
- Each service page lists exactly what you receive; none of it is an optional extra.
- You talk to the engineer doing the work.
03 / When we would say no
When we are the wrong choice
- A rule, a database query or a spreadsheet would do the job. You hear that when we scope the work, and you owe us nothing.
- There is no data to learn from, and no way to collect it.
- Nobody can say what a good result looks like, so nothing can be measured.
- A managed product you already pay for covers the need.
Next
Not sure which one fits?
Many problems touch two of these. Describe the problem in two or three sentences and we will say which part we would start with, or that AI is the wrong tool.