We built Aletheia after finding that the AI safety field lacked a systematic, production-ready methodology for measuring behavioral failures at deployment time. Existing approaches (red-teaming, manual audits, one-off benchmarks) produce results that don't transfer to production and can't be tracked over time.
By grounding our 9 signatures in a corpus of 2,571 real-world AI incidents from AIID, AVID, and the MIT AI Risk Repository, Aletheia provides the first empirically-validated behavioral taxonomy for AI observability — not just red-teaming or vibe checks, but measurable, reproducible failure rates with statistical process control.
The architecture is production-ready: BYOK means you pay only for the observability layer. Probes run against your endpoint, scores stream to your analytics environment, and SPC control limits alert you to behavioral drift before it impacts users.
"The 9 signatures were derived from a systematic review of 8 fundamental AI system interfaces: language generation, tool use, memory, multi-agent coordination, knowledge retrieval, code execution, user interaction, and output formatting, then validated against every incident in our 2,571-case corpus."
Aletheia Research, 2026
