AI systems are getting better at passing benchmarks. But what happens when they fail in the real world?
A harmful answer.
A misleading recommendation.
A biased decision.
An unexpected action.
These failures happen in real interactions with AI systems, but many are never systematically captured, connected, or learned from.
Michu AI is building the missing accountability layer for AI.
We are developing an open, research-driven infrastructure that turns real-world AI failure experiences into structured accountability intelligence.
Michu AI is designed to help capture and understand what conventional evaluations may miss after AI systems reach real users and environments.
- Report — Capture real-world AI failures and unexpected behavior
- Understand — Structure and classify what happened
- Connect — Link related failures and identify recurring patterns
- Detect — Surface signals and emerging risks
- Learn — Create a continuous feedback loop between AI users and AI builders
The goal is not simply to collect complaints.
It is to make scattered experiences observable, analyzable, and useful for research, evaluation, safety, and governance.
AI evaluation often asks:
How capable is this system?
Michu AI is interested in another question:
What is this system getting wrong in the real world—and what can we learn from it?
We believe real-world AI behavior can provide an important source of evidence for understanding how AI systems behave beyond controlled evaluations.
Michu AI is currently in active development, with ongoing work on the technical and research foundations needed to make real-world AI failure data useful and reliable.
Capturing what AI gets wrong. Learning from it. Helping build better AI.
Learn more about Michu AI on our official website.
