AI safety testing
60s
See your first result
1 upload
Start with your current model
Clear report
Know what needs attention
We break your AI before your customers do.
Upload your model. We find the failure cases you would rather catch now than after launch.
What MeghyanAI does
Built for the people who can't afford to be wrong
M
MeghyanAI Analyst
● Online
For operators and safety teams
Ask it anything about your fleet's risk
Upload your autonomous system's policy and ask MeghyanAI plain questions. It finds the dangerous scenarios your team hasn't thought of yet before your aircraft does.
scenario_api.py
For ML engineers
Stop training on the easy stuff
Your autonomous AI has never seen the scenarios that actually cause failures because they are too rare for random simulation to find. MeghyanAI generates quantum-biased edge cases your model needs before deployment.
Your AI policy · v2.3.1
2 critical failures
4 high severity
7 medium severity
Needs remediation before deployment
For founders and CTOs
One number that tells you if your AI is ready
Before you demo to investors, before you go to market, before you hand controls to an autonomous system, know your risk score. Anything above 70 means the model needs more work.
M
Verification Report
Signed
Risk score
73 / 100
Scenarios tested
500
State space coverage
34%
Critical failures
2
Quantum backend
IBM Qiskit Aer
Teacher accuracy
92.6%
Download PDF
decision_traces.jsonl
For certification engineers
The evidence your regulator actually wants
Stop manually documenting test coverage. MeghyanAI generates the evidence pack: failure catalog, decision traces, coverage metrics, and a signed report formatted for real review workflows.
Under the hood
Powered by real quantum computing research
IBM Qiskit
Quantum circuits run on Qiskit Aer statevector simulation. No hardware account required for the demo.
Quantum Tree distillation
Teacher, quantum refinement, then student. A three-layer hybrid pipeline behind every run.
ICAO-standard failure criteria
Separation loss uses real aviation thresholds so the results map to practical safety review.
Penn State ICDS research
Developed on research computing infrastructure as a real model pipeline, not a marketing mock.
DO-178C aligned output
Evidence packages can be shaped into formal review artifacts, decision traces, and coverage reports.
Black-box compatible
Upload ONNX, PyTorch, or packaged policy artifacts without exposing internal model weights.
Live demo
See it in action, no signup required.
Pick a scenario. We run the real Quantum Tree pipeline. Results in about 60 seconds.
Teacher analyzing
Quantum refining
Student scoring
How it works
Three real stages, one product workflow.
Classical Teacher
Learns the behavior surface and flags where risk is likely to hide.
Quantum Refinement
Uses Qiskit to bias the search toward rare, safety-critical failures.
Distilled Student
Turns the run into a reusable testing layer for continuous assurance.
Packages
Sellable workflows backed by the real pipeline.
$2,500/run
Single-policy stress test with failure ranking, risk score, and decision traces.
- Fast red team execution
- Top failure catalog
- Launch-readiness review
$500/month
Quantum-biased scenario generation for training and regression pipelines.
- Batch scenario delivery
- JSON-ready outputs
- For ML and simulation teams
$10,000/audit
Evidence package for formal review, audit preparation, and safety stakeholders.
- Comparator evidence
- Coverage and trace outputs
- Boardroom-ready reporting
Built for aviation first
Collision avoidance, traffic separation, and obstacle-driven decision systems.
Powered by research
IBM Qiskit · Quantum Tree distillation · Penn State ICDS research.
Ready for buyers
A clean product surface with gated access for customers, teams, and audits.