Service
Machine learning & applied AI
Models and language-model systems taken from problem framing to production, with the evaluation needed to prove they hold up.
What we do
We start by asking whether a model is the right instrument. Plenty of problems presented as machine learning are reporting problems, rules problems or data-quality problems, and saying so early is the cheapest thing we can do for you.
When a model is the right answer, we build the pipeline, the evaluation and the deployment path together, so the system can be retrained and re-measured by your team rather than by us.
How we keep it honest
Before training anything we write down what a wrong answer costs — who notices, how quickly, and what it means commercially. That single paragraph decides how much human review the system needs and what “good enough” is.
Every model we deploy has an off switch and a defined behaviour when it is off.
What you get
- A written feasibility view, including the case for not building it
- Re-runnable pipelines with evaluation attached and results stored
- Monitoring of input distributions, output quality and cost
- Language-model systems with retrieval, evaluation harnesses and spend controls
Who it is for
Teams with a promising prototype that has not survived contact with real data. Businesses automating document-heavy or triage-heavy work. Companies who have been sold an AI roadmap and want an independent read on it.