AI that does a job.
Models and intelligent features shipped inside real products, with real evaluation.
A demo is not a product. Start from the evaluation, not the model.
The problem
Anything looks impressive on five hand-picked inputs. The question is what happens on the thousandth one, whether you can tell when it is wrong, and what it costs per request. Most AI projects stall exactly there.
Our approach
We define what a correct answer looks like before we build, so quality is measured instead of argued about. Then we ship the smallest version that does the job and improve it against that measurement.
- 01An evaluation set drawn from your real inputs, before the build
- 02Retrieval and context design before model selection
- 03Cost and latency budgets treated as product requirements
- 04Human review paths for anything consequential
What this includes
- Product assistants and copilots
- Retrieval-augmented systems over your own content
- Classification, extraction and document processing
- Workflow automation with human review
- Evaluation harnesses and regression testing
- Model selection, prompting and fine-tuning
- Inference cost and latency optimisation
- AI features inside existing products
How this runs.
- 01
Think
The decision or task being automated, and how failure is detected.
- 02
Design
Interaction, review paths, and what happens when the model is unsure.
- 03
Build
Pipeline, retrieval, evaluation harness and the product surface.
- 04
Launch
Staged rollout with quality and cost monitored from the first request.
- 05
Keep alive
Re-evaluation as inputs drift and models change.