Make AI Agents Faster Without Lowering the Bar

Latency improvements only matter if the agent still completes the job. ProofMap helps teams qualify faster models and prompts before rollout.

Why Choose ProofMap

Compare response quality and execution behavior across lower-latency model options.

Use fallback mappings so expensive or slower models handle only the cases that need them.

Catch regressions in tool use, structured outputs, and final answers before latency changes ship.

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.

Can we optimize latency and quality together?

Yes. ProofMap evaluates the faster runtime against the same objective criteria as the baseline.

What if the fastest model fails some tasks?

Use it only for the criteria it passes and keep fallback routing for the tasks that need a stronger runtime.

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.

Qualify faster runtimes before making them the default.