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COMOX AI

Quality you
can inspect.

Clear criteria before work begins. Review matched to the task. Evidence that stays connected to the delivered result.

Built into the workflow.

Calibrate on the task.

Work through representative examples before production. Agree the rubric, reference answers where available, and how to handle ambiguous cases.

Match review to the risk.

Define independent review, adjudication, and spot-audit requirements in the scope. Inspect agreements as well as disagreements to catch shared mistakes.

Keep uncertainty visible.

Record unresolved judgments and exclusions with their reasons. Distinguish automated checks, model suggestions, and human decisions in the agreed output.

Measure the right thing.

An annotation accuracy check, a reviewer agreement measure, and a model evaluation answer different questions. We agree which measurements fit your task and how they will be interpreted before the work starts.

A difficult benchmark can expose model failures. Its difficulty alone does not establish annotation accuracy or prove an improvement in model performance.

Explore Comox Evaluate

An inspectable record.

Project Vista combined independent benchmark review, adjudication, and senior spot-audits. The delivery included reviewer determinations, documented corrections, and reasons for exclusions.

The case study describes that engagement. Its published figures include their scope and sources; they are not a blanket quality guarantee for other tasks.

Read Project Vista

Published figures and sources ↗

Define the quality standard for your next dataset.

Discuss a pilot