Bring Evidence to LLM Vendor Negotiations

Vendor claims are useful; your own workflow data is better. ProofMap helps teams compare providers on quality, cost, and operational fit.

Why Choose ProofMap

Evaluate providers against the tasks your AI system actually performs.

Show where alternatives already pass and what savings or risks a switch would create.

Use failure evidence to justify staying with a more expensive model when quality requires it.

Decision area	Ad hoc workflow	ProofMap
Model or provider change	Teams compare demos, skim logs, and make a judgment call under pressure.	Run baseline-versus-challenger evaluations and see pass/fail evidence before a change ships.
Cost and performance tradeoff	Savings, latency, and quality are discussed separately, usually without a shared source of truth.	Compare quality evidence with cost, runtime, and fallback options in the same qualification workflow.
Production approval	Prompts and model choices move through informal review or one-off scripts.	Only qualified prompt packages and runtime mappings are promoted for production use.
Incident readiness	Fallbacks are invented after prices change, providers fail, or behavior drifts.	Backup models, prompt mappings, and fallback policies are qualified before they are needed.

How does ProofMap help in vendor negotiations?

It gives you workflow-specific quality and cost evidence instead of relying only on vendor benchmarks.

Can it justify not switching to a cheaper vendor?

Yes. Failed criteria and fallback needs can explain why the premium runtime is still necessary.

Who is this for?

Teams building AI agents or LLM-backed workflows that need evidence before changing prompts, models, providers, or fallback policies.

What does ProofMap produce?

A qualification trail: objective-bound evaluations, failure evidence, recommendations, and approved prompt or runtime mappings for production use.

Compare vendors using your own objectives, not generic demos.