Know Whether AI Workflows Are SLA-Ready
SLA commitments need evidence. ProofMap helps teams understand which AI workflows are ready for stronger promises.
Get StartedWhy Choose ProofMap
Measure reliability
Evaluate quality and runtime stability across critical scenarios.
Plan fallback paths
Qualify backup models and degraded modes before provider or latency issues happen.
Support commitments
Use evidence to decide which workflows can support customer-facing guarantees.
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
Can AI workflows have SLAs?
Yes, but commitments should be based on tested reliability, fallback readiness, and clear limits.
What if a workflow is not ready?
ProofMap helps identify whether the gap is prompt quality, model choice, provider risk, latency, or tool behavior.
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.
Prepare for stronger promises
Use qualification evidence before committing to AI SLAs.
Start qualifying prompts