Artificial Intelligence & Automation
Evaluation & quality of AI systems
AXENEO supports organisations in defining evaluation criteria, designing test set-ups and tracking the quality of AI systems, from prototype to production.
IN ONE SENTENCE
Make AI uses dependable: measure what they produce, and keep watching over time.
THE PROBLEM
The situations that bring us in
- 01
The expected quality is not defined before the first tests.
- 02
Nothing distinguishes a convincing demonstration from a dependable production use.
- 03
The test sets do not reflect the cases actually met.
- 04
A change of model, prompt or data passes with no non-regression check.
- 01THE PROBLEM
A system that convinces in demonstration, without anyone being able to say on what conditions it is judged dependable — nor how anyone would notice if it stopped being so.
- 02WHAT AXENEO DOES
We define the acceptance criteria with the business, build reproducible test sets, combine automatic and human evaluation, then install supervision in production.
- 03THE RESULT
Quality that is measured, changes controlled by non-regression tests, and drift detected before the users notice.
What we do
Define and put to the test
- Evaluation criteria and indicators fitted to the use case
- Explicit acceptance criteria, settled before the tests
- Versioned test sets, replayable as they stand
- Automatic and human evaluation, combined
Operate and maintain
- Non-regression testing on the model, the prompts, the data and the tools
- Supervision in production and drift detection
- Tracking the stability of answers over time
- Improvement loop fed by the evaluation results
The Artificial Intelligence & Automation method
- 01
Identify
We start from the processes and from real points of friction. A use case with no business owner does not enter the portfolio.
- 02
Scope
Perimeter, data involved, success criteria, acceptable quality threshold and stopping conditions. Success is defined before the work starts.
- 03
Prototype
A short prototype, on real data, evaluated against a representative test set. The aim is to decide, not to convince.
- 04
Integrate
The use is plugged into the process and the existing applications. Without integration, adoption does not happen.
- 05
Industrialise
Operations, supervision, costs, scaling, version and model management. A use in production is a service.
- 06
Govern
A usage framework, quality control over time, traceability and accountabilities. That is what makes extending the scope possible.
USE CASES
Where we step in
Qualifying a business assistant
Establish what a good answer means for this trade, build a representative test set and measure before release rather than after the first complaints.
Test strategy for a RAG set-up
Separate what belongs to document retrieval from what belongs to phrasing, so as to know which of the two to fix when the answer is wrong.
Checking after a change of model
Replay the test set to locate the gap, tell a real improvement from a regression, and decide knowing what has changed.
Tracking quality in production
Watch the chosen indicators, spot slow drift and trigger a review before the use degrades.
Contact
Evaluation & quality of AI systems: let’s talk about your context
We start from your process. A short conversation is enough to tell a use case that will hold in production from an experiment that will stop at the prototype.