Evidence
Case Studies
FailureModes.ai case studies show how real AI systems fail, how those failures are detected, and what mitigations reduce recurrence. Each case study should focus on the failure pattern, not just the product implementation.
In scope
What each case study covers
Customer context
Industry, scale, and operating environment.
AI system type
LLM, RAG, agent, or composite system.
Business workflow
The real workflow the AI system supports.
Failure modes discovered
Recurring patterns observed in production or pre-launch.
Detection approach
Evals, traces, monitors, or red-team methods used.
Severity and impact
Customer, compliance, financial, or operational impact.
Mitigations added
Controls, prompts, schemas, retrieval, and policy changes.
Outcome
Measured change in reliability and incident rate.
Lessons for other teams
Patterns that generalize beyond this customer.
Where FailureModes.ai fits
Case studies are written to be useful to other AI teams — focusing on the failure pattern and the mitigation rather than vendor mechanics.
Related