Know the 'why' behind every outcome.
AI-powered root-cause intelligence that explains, compares, and solves complex issues through deep explainability and similarity analysis.
Understand feature importance and causal factors driving every outcome.
Find similar past events and learn from previous resolutions.
Get specific, prioritized recommendations based on root-cause analysis.
Deep explainability and causal intelligence for AI decisions
Intercepts AI model outputs and extracts feature importance, confidence scores, and input context for every prediction.
Applies causal reasoning to identify root drivers, performs similarity search against historical cases, and constructs explanation narratives.
Delivers human-readable explanations, recommended actions based on similar past outcomes, and full audit trails for compliance.
Advanced causal reasoning that transforms black-box predictions into transparent insights
Builds causal graphs to understand the "why" behind predictions, not just correlations
Finds analogous past cases using semantic embeddings and multi-dimensional similarity
Assesses the reliability of explanations based on data quality and model uncertainty
Transforms technical insights into clear, actionable narratives for stakeholders
Model-agnostic explainability system combining SHAP, LIME, and causal inference for deep insights
Multi-method approach
Vector-based retrieval
Sub-100ms overhead for production ML systems with parallel processing
GDPR Article 22, FCRA, and industry-specific audit trail generation
What-if scenarios showing how inputs affect predictions with confidence scores
Built on Decision Intelligence
Each of these is Decision Intelligence pointed at one kind of problem — same core, same audit trail, same deployment — tuned for a domain. They are in development with design partners and are not sold separately.
Know why your agents did what they did.
Traces a decision back through every hand-off between agents and tools in a multi-agent workflow.
Compliance evidence, generated as you build.
Turns explainability and monitoring data into continuous, regulator-ready compliance documentation.
An answer clinicians can trust — and defend.
Wraps clinical AI tools with plain-language reasoning and a defensible audit trail.
Investigates first. Acts only within limits you set.
An autonomous agent that investigates incidents end-to-end and takes pre-approved remediation steps.
Stress-tests your AI before your customers do.
Adversarially probes your own models and agents, and reports what broke and why.
Test the change before it touches the real system.
An explainable digital twin of your operational system, so changes can be simulated first.