About

What we’re building

LLM Prover is the measurement layer for AI. It helps teams prove which models actually perform on their own data, check whether an AI’s output is correct, and catch the day a model quietly changes. Not on someone else’s leaderboard. On your task.

We built it because the usual way of trusting AI is broken. Leaderboards measure generic tasks, and a confident answer is not the same as a correct one. Your work is not generic, so your measurement should not be either.

Agentic first

We are built for agents, not just dashboards. Connect over MCP and use LLM Prover as a measurement and validation step inside your own agent workflows. Your agent can compare models, score an output against your standard, and check an answer before it acts on it, all without leaving its workflow.

There are two levels of test. Model evaluation is the unit test: it checks one step, a single call to one model. Agent evaluation is the integration test: it checks a whole agent workflow, step by step, and shows where it wasted money, time or effort. Both run on one engine. See agent evaluation.

Why measurement is the job now

As AI generates more, the scarce and valuable work shifts from producing output to checking it. A model can write the answer, but someone still has to be sure it is right, and that step is where most teams are flying blind. That is the job we exist to make easy: measure quality against your standard, see what it costs, and know when it drifts.

How it works

You bring your prompts, your rubrics, your documents. We run them against every major model in parallel, score the results against the standard you define, point to the part of your setup that caused a weak answer, and track quality over time. It works from the dashboard, the API, or straight from your AI agent.

We are neutral, by design

We take no money from any AI provider. We do not sell you a model or push you toward one. We are an independent instrument: we help you measure and improve your AI, and you stay in charge of your results. That neutrality is the whole point. You cannot trust a tool to grade itself, and you should not have to.

Before you sign up for anything, you can try the idea. We publish a set of free, open source tools for working with AI models: compare two models on your own prompt, check an answer against a source, test how consistent a model is, and estimate cost. They run on your own API key, and nothing is sent to our servers. Explore them at llmprover.pysolvr.com/tools.

Who it’s for

Teams that ship AI-powered products and need to know, with data, that the model they chose is still the right one next week. And anyone who has to answer the harder question: is this AI output actually correct?