Explainable alert triage
Help analysis teams examine classified records and the feature combinations supporting each result. Bring useful context into their review workflow.
NEURYTIKA · CELESTE ECOFLYERS
Explainable AI for Intelligence & Decision Support
Bring the evidence behind classifications into your analysts’ workflow. Evaluate Neurytika on your structured data and give your team a clearer basis for review.
Explore an Explainable AI Pilot ↗See the 60-second example →Understand every alert. Make better-informed decisions.
Investigate unusual situations
Evaluate classification against categories defined with your domain experts. Inspect unusual records, compare outcomes and examine the evidence behind incorrect alerts.
Human-led decisions
Give analysts an explained classification to review alongside their domain knowledge. Scope how results, model versions and explanations will be documented in your existing systems.
An alert with a reason to investigate
ILLUSTRATIVE SCENARIO · FICTIONAL EQUIPMENT DATA
A monitoring station needs an engineering review
- Observed record: elevated temperature, intermittent signal and a recent maintenance intervention.
- Evaluation question: which combinations in labelled historical records support the “engineering review” category?
- Analyst review: inspect the combinations and their statistical support, compare alternative classes, then decide the next step.
The pilot produces the measured classifications and explanations from your representative dataset.
An evaluation built around your analysts
For analysis centres, infrastructure operators and integrators: choose one structured dataset, one classification task and the decisions it should inform. Evaluate accuracy, false alerts and the usefulness of explanations with the people who will use them.
Evaluate ERC on your analysis datasets ↗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.