Interrogait offers a behavioral‑assurance platform that rigorously tests AI models with adversarial, counterfactual, and faithfulness probes to verify that both decisions and their explanations are trustworthy. The system generates immutable, timestamped evidence bundles and a continuous trust score mapped to over 24 regulatory frameworks, giving compliance and risk teams audit‑ready documentation for high‑impact AI deployments.
Funding
Funding not disclosed
Founders
Product
Problem
AI systems are increasingly used in high‑stakes business processes, but existing explainability tools only reveal why a model gave a particular output and do not verify whether the explanations themselves are trustworthy. This creates regulatory, legal, and reputational risk because organizations lack defensible evidence of AI behavior, especially for non‑deterministic models such as large language models.
Solution
Interrogait provides a behavioral‑assurance platform that tests AI models under adversarial and counterfactual conditions to surface hidden risks such as hallucination, deception, bias, and explanation drift. The system treats explanations as claims and subjects them to deep probes—including adversarial interrogation, targeted ablation, and faithfulness testing—to generate immutable evidence bundles. These artifacts are timestamped, traceable, and mapped to over 24 regulatory frameworks, enabling audit‑ready documentation. A continuously updated trust score aggregates behavioral evidence, giving compliance teams a quantifiable risk signal that reflects model truthfulness rather than pure accuracy. The platform delivers a web‑based dashboard where users can monitor trends, drill into root causes, and export evidence for regulator review.
Target Audience
Primary customers are enterprise risk, compliance, and AI governance teams in regulated industries such as finance, insurance, healthcare, and any organization deploying high‑impact AI decision systems.
Features
- Adversarial interrogation that challenges unstructured models with signal‑driven questions to detect deception, hallucination, and drift
- Targeted ablation tests for structured decision systems, creating counterfactual scenarios to expose hidden bias and justification drift
- Faithfulness testing that validates whether model explanations accurately reflect underlying behavior
- Immutable evidence bundles that record inputs, outputs, explanations, and behavioral signals with timestamps for auditability
- Trust scores that aggregate behavioral evidence into a defensible risk metric, updated as new tests run
- Pre‑mapped compliance library covering 24+ regulatory frameworks and sector‑specific controls
- Dashboard with trend monitoring, root‑cause analysis, and exportable audit artifacts