Make Enterprise AI Pilots Measurable
Pilots stall when success is subjective. ProofMap turns pilot readiness into objectives, evaluations, and evidence-backed decisions.
Get StartedWhy Choose ProofMap
Define success upfront
Convert pilot goals into criteria that can be tested across prompts and runtimes.
Control pilot access
Use scoped MCP tools and approved mappings so pilot agents stay inside boundaries.
Decide with evidence
Know whether to expand, adjust, or stop the pilot based on measured outcomes.
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 AI pilots fail?
They often lack clear success criteria, repeatable evaluation, tool-access controls, and a path from demo to production.
When should ProofMap be introduced?
Before pilot launch, while objectives and success criteria are still being defined.
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.