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.
EN UNE PHRASE
Make AI uses dependable through evaluation, testing and supervision.
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
- Realistic test sets, versioned and replayable
- 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.