Evaluate AI Release Candidates Before They Ship
AI releases need more than smoke tests. ProofMap gives teams a repeatable way to qualify prompt, model, and tool behavior before promotion.
Get StartedWhy Choose ProofMap
Test the candidate
Run objective-bound evaluations against the exact prompt and runtime package planned for release.
Compare to baseline
See where the candidate improves, regresses, or needs a fallback mapping.
Promote with evidence
Approve only the release candidate that passes the criteria that matter.
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 release candidate?
A proposed combination of prompt, model, runtime mapping, tool access, and configuration that may be promoted to production.
When should it be evaluated?
Before any production rollout, especially when prompts, models, tools, context, or fallback routes changed.
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.