Service
Evaluation & Assurance
Retrieval evaluations, faithfulness scoring, rule-based second opinions, quality triage without ground truth. We put numbers on an AI system before launch and build the tests that keep it honest after.
What we do
- Retrieval evaluation
- Context recall and faithfulness measured with a judge, on your own questions.
- Rule-based second opinions
- A deterministic screener beside the model, so disagreements surface.
- Quality triage without a reference
- Score every output, sort worst first, with a confidence band that is load-bearing.
- Capped review
- The model may adjust a computed answer only within a stated tolerance.
- Test suites
- Tests that need no key and no network, run before every change.
Client stories
Healthcare & Life Sciences
Triage for segmentation masks with no answer key
A tool that scores every mask against an estimate of what an expert would say, with a confidence band that sets the colour, and sorts the review queue worst first.
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Healthcare & Life Sciences
A first pass on hundreds of standards
A regulatory assistant that reads a device description, works through the workbook of recognised standards with tools the reviewer can see, and fills in the screening columns the team used to fill by hand.
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Industrial & Scientific Distribution
A front desk that answers every call and knows the catalogue
A phone agent that rings the owner first and answers when nobody can, and a website assistant that shares the same brain, the same catalogue and the same booking tool.
Read the storyRelated insights
Report · Energy, climate & carbon markets
What a carbon credit actually costs
The listed price is not the price. The brochure is not the integrity. And the trade history, which is public, is the number nobody looks at. A sourcing engine built from public venues shows what a buyer should actually expect to pay for a credible tonne.
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Article · Logistics
The pricing engine should not be the language model
A dispatcher pasting a load-board posting into a chatbot gets a confident number and no way to know where it came from. The fix is not a better prompt. It is a quote built line by line by code, with the model limited to reading and reviewing.
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Report · Healthcare & life sciences
Grading a segmentation mask when nobody knows the right answer
Judging a mask against an expert's is arithmetic. The case teams actually face is a model producing thousands of masks with no reference at all. The honest answer is triage: a score, a confidence band that decides the colour, and a queue sorted worst first.
Read the reportLet's find the hours
Tell us which workflow eats the day. We will tell you whether it is worth building.