Most AI failures trace back to the same root causes: the wrong approach chosen for the problem, a model never properly evaluated for the actual task, or data that doesn't reflect reality. Mizan-ly closes that gap — before it becomes a production problem.
Independent evaluation across model behavior, data quality, and real-world task fit — delivered as findings your team can actually act on.
Does the model actually solve your problem — not just a proxy benchmark? Rigorous evaluation against your specific task requirements.
Systematic stress-testing to surface where the model breaks — edge cases, distribution shifts, adversarial inputs — before production.
Side-by-side evaluation of candidate models on your data and task — including open-source vs. commercial trade-offs, with evidence.
Assess quality, label consistency, coverage gaps, and distribution issues that cause models to underperform, hallucinate, or degrade.
Understand what your data is actually worth — what signals it holds, what's missing, and whether it supports your task.
Build held-out evaluation sets that reflect your real-world distribution — not proxy benchmarks that fail in production.
Validate quality, diversity, and coverage of generated data before it enters your training pipeline.
Define metrics that reflect success in your use case — classification, extraction, summarization, RAG, generation, or custom tasks.
Evaluate the full model + data + pipeline together — including RAG retrieval, structured output, and human-in-the-loop checks.
Clear explanations of model behavior for engineers, leadership, and non-technical stakeholders.
Structured formats designed to fit where you are — from a quick feasibility check to ongoing evaluation support.
Go/no-go assessment — is your data, task, and use case actually ready for AI? Risk summary delivered before any investment is made.
Targeted model or data review — what's working, what isn't, and exactly why. Delivered as a plain-language actionable report.
Custom benchmarks, evaluation datasets, and automated pipelines — fully documented and owned by your team after delivery.
Regression testing, monitoring, and expert interpretation as models and data evolve.
Clear, documented findings — not vague observations. Evidence your engineers can act on and your leadership can understand.
Specific capability gaps, failure modes, and whether the model fits the task — with evidence.
Quality issues, coverage gaps, and distribution problems traced to their source.
Metrics, datasets, and test suites your team can run on every model or data update going forward.
A clear, evidence-backed answer to "does this model work for our problem?"
Findings explained plainly for leadership, customers, or regulators — not just engineers.