Practice AI Incident Response Before It Matters
Teams rehearse infrastructure incidents. AI runtime incidents deserve the same muscle memory, with fallback mappings and evaluation suites ready.
Get StartedWhy Choose ProofMap
Test outage playbooks
Simulate provider failures, rate limits, and degraded models before users are affected.
Validate fallback routes
Confirm which workflows can move to backup runtimes and which should degrade safely.
Improve response time
Turn drills into evidence, regression tests, and updated approval policies.
Comparison
| Workflow | Without ProofMap | With ProofMap |
|---|---|---|
| Evaluate AI behavior | Teams rely on demos, logs, and manual spot checks. | Run objective-bound evaluations against prompts, models, MCP tools, and runtime mappings. |
| Handle change | Prompt, model, context, schema, memory, or vendor changes create hidden regressions. | Compare candidates to baselines and promote only qualified packages. |
| Support developers | Developers trace failures across tools, providers, data, and one-off scripts. | Failures become repeatable tests with clear evidence and recommended fixes. |
| Control production risk | Fallbacks, permissions, and degraded modes are invented when pressure hits. | Approved mappings and fallback paths are ready before launch, incidents, or migration deadlines. |
Frequently Asked Questions
What is an AI incident drill?
A rehearsal for AI-specific failure modes such as provider outages, model regressions, tool misuse, cost spikes, or unsafe outputs.
How does ProofMap help?
It provides the evaluation suites and fallback evidence that show whether the response plan actually works.
How does this save developer time?
It makes evaluation, debugging, approval, and regression testing repeatable instead of forcing developers to rebuild evidence for every AI change.
What does ProofMap produce?
ProofMap produces objective-bound evaluations, failure evidence, recommendations, and approved prompt or runtime mappings for production use.