Mitari Research develops methods and infrastructure for domain-aware verification: representing intent, evaluating evidence, detecting silent failures, and improving development loops involving both people and AI agents.
Our work sits at the intersection of applied machine learning, data systems, evaluation, and developer infrastructure.
Our research spans intent representation, evidence evaluation, independent verification, and feedback loops. The same two-layer strategy shapes what we build: a forthcoming build-with-evidence layer, verified by Fathom.
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 independentlyHow multiple models, deterministic checks, context, and structured evidence can produce more dependable judgments than a single-pass review.
How teams can define what should be true, collect the results of existing tests and harnesses, and determine whether those results support a change.
How coding agents can work with explicit intent, constraints, system context, and independent verification.
How accepted findings, rejected findings, fixes, production outcomes, and practitioner feedback can improve future verification.