Correct, explainable and auditable AI outcomes

Your AI agents are running on legacy infrastructure that prevents provable and traceable troubleshooting — Prove AI is here to help.

AI-native troubleshooting

AI token intelligence

Enter the M.A.S.E. on Discord!Join a community of AI engineers discussing their primary AI troubleshooting challenges with multi-agent systems. Join

Why Prove AI?

Non-deterministic AI systems need more intuitive tooling

AI systems are being trusted with more important projects, but basic questions remain hard and time-consuming to answer, including the anticipated cost per workload and how and why a given agent reached a decision.

01
Actionable intelligence for every token
Across multiple platforms and vendors, via a single hub.
02
Evaluate AI decisions at runtime
Prove how and why your AI arrived at a specific outcome.
03
Catch silent regressions
Replay every candidate fix against your trace archives before it ships.

Our vision

Prove AI is ushering in more sustainable management framework for multi-agent AI systems

We’re building Prove AI because we see an imminent fork in the road: token usage isn’t sustainable, and AI is about to get a lot more expensive.

Soon, you’ll need to ensure that every AI-driven outcome is traceable to a clear source, and that it’s being reached in a cost-efficient manner.

Keeping a human in the loop is critical to realizing AI’s potential in a predictable and cost-controlled manner. With Prove AI, you can surface, incorporate and operationalize human expertise within AI systems.

Meet the team

Built by a team that knows how to ship AI at scale

Our team has automated billions of dollars in purchasing decisions at Amazon using reinforcement learning, architected production multi-agent infrastructure for Fortune 500 deployments and scaled engineering organizations at a YC-backed fintech unicorn. We’ve felt this problem firsthand — and we’re building the tool we wish we’d had.

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