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PH1 core capbility
YEARS EXPERIENCE
3 to 5 years
TYPICAL CLIENT
Head of AI, Chief Risk Officer, VP Product, General Counsel
NECESSARY TIMELINE
Less than 2 months
Our POV
The failures that damage a brand are rarely the rarest ones. They are the failures that happen in front of customers, in high-volume moments, in ways people screenshot and share.
Ranking by probability alone sends teams to fix the wrong things. Ranking by frequency and consequence together sends them to the failures that actually matter.
What We Do
Test adversarially and at the edges. Real workflows pushed into the conditions where the system is least reliable, including the inputs a frustrated or hostile user would try.
Catalogue what we find. The specific prompts, inputs and outputs that fail, documented precisely enough for engineering to reproduce.
Rank by frequency and brand risk. So guardrails get built for the failures that will be seen, rather than the ones that are merely interesting.
What We'll Deliver
Catalogue of reproducible failure cases with exact inputs
Severity ranking by frequency and brand or regulatory consequence
Analysis of the conditions that produce each failure
Guardrail and mitigation recommendations in priority order
Regression cases your team can run on every release
A summary suitable for risk, audit or board reporting
When This is Essential
Before launching an AI feature to a large or public audience
When a failure has already been seen publicly and the extent is unknown
When risk, audit or legal need evidence of what can go wrong
Before a model or vendor change that could alter behaviour
When guardrails exist but nobody has tested them adversarially
Combine With These Services
Golden Dataset Research — define correct before cataloguing incorrect
AI Evals & Output Fine-tuning — turn the catalogue into regression tests
AI Service Design & Blueprints — design the recovery paths the failures need
Part of AI Product Evaluation.
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Submissions