Frame your problem
Choose a decision to support, the users involved and the available structured data.
NEURYTIKA · CELESTE ECOFLYERS
Evaluate Explainable AI on Your Own Data
Turn a business question into a concrete evaluation. Work with our team to measure ERC on representative data, examine its explanations and plan your next step.
Explore an Explainable AI Pilot ↗See the 60-second example →A focused project. A clear decision at the end.
Agree success criteria
Set relevant measures together: class quality, false alerts, explanation usefulness and deployment requirements.
Evaluate representative data
Prepare features and labels, define a suitable evaluation split and evaluate ERC on reserved examples.
Compare relevant baselines
Compare with your current approach and suitable reference methods using the same evaluation protocol.
Review results & explanations
Inspect classifications, feature combinations and errors with domain experts and future users.
Plan your integration
Receive findings and a proposed next step covering interfaces, infrastructure, validation and team support.
What your team receives
- An agreed scope and evaluation protocol.
- A results report with baseline comparisons and analysis of errors.
- Explained example classifications reviewed with your experts.
- An integration recommendation and a scoped commercial proposal for the next phase.
Scope, schedule, fees and data-handling arrangements are agreed in your tailored proposal. Evaluation can be organised around client-controlled infrastructure or a preferred cloud environment.
Choose your starting point
LET’S DEFINE YOUR PILOT
Tell us what you want to improve.
Share a short project summary and the types of data available. We will use this first exchange to shape your evaluation. Dataset transfer and access arrangements are agreed together in the next step.
Evidence you can inspect. Expertise you can work with.
Neurytika is developed by Celeste EcoFlyers in France. Its Evidence Ratio Classifier (ERC) represents relationships between discrete feature combinations and target classes, making their statistical support available for inspection. The research is published in Neurocomputing.