Harden AI Demos Before Production
A demo can impress and still fail in production. ProofMap helps teams turn promising prototypes into evaluated, controlled workflows.
Get StartedWhy Choose ProofMap
Move beyond happy paths
Test edge cases, bad inputs, tool failures, and realistic user behavior.
Add production controls
Qualify prompts, models, MCP permissions, fallback routes, and cost limits.
Decide readiness
Know what must change before the demo becomes a production feature.
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
Why do AI demos fail in production?
They are often tuned for ideal examples and lack coverage for edge cases, permissions, cost, latency, and failure recovery.
When should hardening start?
As soon as a prototype is considered for customer or internal production use.
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.