AI is accelerating how data pipelines, analytics, models, and intelligent systems are built. Trust is becoming the bottleneck.
Data + AI systems can execute successfully while producing invalid, misleading, or irreproducible results. A pipeline can succeed. Tests can pass. The output can look plausible. The underlying logic can still be wrong.
Mitari is building the independent verification layer around this work: infrastructure that makes intent explicit, gathers evidence from the tools teams already use, and helps determine whether a change is supported before and after it ships.
Fathom, our product available today, verifies pull requests, repositories, and submitted files. Our next product will move the same discipline upstream into local and agentic development.
A join can silently double-count. A schema change can quietly corrupt a downstream table. An aggregation can drop late-arriving rows. A model can leak future information, rely on a broken assumption, or report performance that will not hold outside the development environment.
These failures span the whole stack — from the pipelines that move and transform data to the models built on top of it. They rarely look broken.
The failure ships and surfaces only later — as business, financial, operational, or regulatory damage.
Tests and assertions are essential, but they primarily catch failures someone anticipated and explicitly encoded.
Data-quality tools check whether data satisfies declared expectations. General-purpose developer tools check whether code compiles, runs, and follows common engineering conventions.
The most dangerous failures often live underneath those checks: the transformation computes the wrong thing, the experiment answers the wrong question, the model learns from information it should not have, or the methodology no longer supports the conclusion.
Mitari closes that gap by judging whether the reasoning underneath the code is sound.
Fathom verifies Data + AI changes today. A build-with-evidence layer is coming soon to move that verification upstream — one strategy, two layers.
People and agents work with explicit intent, durable context, evaluations, and supporting evidence.
Passes the change and evidence forwardIndependently verifies the change, surrounding logic, and available evidence before it ships.
Verifies Data + AI code independentlyCode can run successfully while computing the wrong quantity, using invalid evidence, or answering the wrong question.
A system cannot be meaningfully verified without understanding what it is supposed to do, under what assumptions, and within what constraints.
Tests, evaluations, metrics, lineage, monitoring signals, and human decisions should not disappear as work moves from local development to review and production.
The system creating a change should not be the only system deciding whether that change is trustworthy.
Teams should be able to keep their existing agents, tests, evaluation harnesses, data platforms, and monitoring tools. Mitari should connect and reason over their evidence.
Findings and proposed changes should be inspectable, reviewable, and honest about uncertainty.
What happens during review and production should improve how future systems are built and verified.
We believe Data + AI teams should be able to move quickly without lowering the standard of evidence applied to their work. Mitari is building the infrastructure to help people and agents develop, deploy, and operate with greater speed, rigor, and confidence.