Keep AI Evaluation Suites Healthy
Evaluation suites get stale unless they evolve with the product. ProofMap helps teams keep coverage useful and tied to production decisions.
Get StartedWhy Choose ProofMap
Make the workflow testable
Turn the evaluation maintenance into objective evaluations that cover prompts, runtimes, MCP tools, and fallback behavior.
Reduce developer guesswork
Give developers concrete failure evidence and reusable tests instead of manual transcript review.
Ship with clearer control
Create fresh coverage for changing AI systems before the workflow reaches more users or more systems.
Comparison
| Need | Manual approach | ProofMap |
|---|---|---|
| Capture behavior | Behavior is inferred from logs, demos, and scattered reports. | Behavior is measured against objective criteria and stored with evidence. |
| Debug failures | Developers chase failures across prompts, tools, data, and providers. | Failures point to criteria, evidence, and candidate fixes. |
| Approve changes | Changes ship when they look plausible enough. | Only qualified prompt packages and runtime mappings are promoted. |
| Maintain confidence | Confidence decays as products and models change. | Evaluation suites evolve with incidents, feedback, and platform changes. |
Frequently Asked Questions
Why use ProofMap here?
Because the evaluation maintenance needs repeatable evidence before teams can trust AI behavior at scale.
How does this save time?
Teams reuse evaluation workflows, approved mappings, and failure evidence instead of manually revalidating every change.
Does this work with MCP?
Yes. MCP tool behavior, permissions, schemas, and error paths can be evaluated alongside prompts and models.
What is the practical result?
Teams get fresh coverage for changing AI systems and a clearer path from experiment to production.
Make AI operations easier to trust
Use ProofMap to qualify the workflow before it becomes operational risk.
Start qualifying prompts