Close the Gap Between Benchmarks and Reality
Generic benchmarks rarely match your product exactly. ProofMap tests model performance where it matters: your prompts, tools, data, and users.
Get StartedWhy Choose ProofMap
Use real objectives
Evaluate models against workflow-specific success criteria instead of generic leaderboard scores.
Explain surprises
Find why a highly ranked model fails your tool use, tone, structure, or domain cases.
Choose pragmatically
Promote the model that passes your workload, not the model with the best headline score.
Comparison
| Moment | Without ProofMap | With ProofMap |
|---|---|---|
| Evidence request | Teams assemble screenshots, anecdotes, and raw logs after the question arrives. | Qualification reports show prompt, model, tool, fallback, and approval evidence. |
| Production change | Prompt, model, schema, or permission changes are reviewed informally. | Changes run through objective-bound evaluations before promotion. |
| Business pressure | Audits, launches, renewals, and customer escalations force rushed AI decisions. | Teams use existing tests and approved mappings to respond with confidence. |
| Developer workload | Developers chase failures across transcripts, tools, providers, and one-off integrations. | Failures become repeatable tests with clear evidence and approved fixes. |
Frequently Asked Questions
Why do public benchmarks mislead teams?
They often measure tasks that differ from your domain, tools, constraints, and failure costs.
Can ProofMap compare benchmark winners?
Yes. Treat each candidate model as a challenger and test it against your own objectives.
What makes this useful for developers?
It turns AI behavior changes into repeatable tests, reduces manual investigation, and provides concrete evidence for prompt, model, MCP, and runtime decisions.
What does ProofMap produce?
ProofMap produces objective-bound evaluations, failure evidence, recommendations, and approved prompt or runtime mappings for production use.