Standardize AI Development Across Teams
As more teams build agents, informal patterns break down. ProofMap gives a shared qualification layer for prompts, models, and tools.
Get StartedWhy Choose ProofMap
Create common gates
Use shared criteria for prompt, runtime, MCP, and fallback approvals.
Support team autonomy
Let teams build while platform owners keep release evidence consistent.
Reduce duplicate work
Reuse evaluations, approved packages, and runtime mappings across related workflows.
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 standardize internal AI development?
Without standards, teams repeat integration work, approve changes inconsistently, and lose track of runtime risk.
How does ProofMap help platform teams?
It creates repeatable evaluation and approval workflows that teams can use without building everything from scratch.
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.