AI Quality Evaluation
Measure whether outputs, retrieval, tool use, conversations and complete workflows actually perform as intended—then turn failures into reproducible tests.
The Challenge
Most AI systems are shipped on the strength of a few impressive demos. In production, the same system faces messy inputs, edge cases and workflows no one scripted—and quality quietly degrades in ways dashboards don't show.
We evaluate quality end to end: individual outputs, retrieval accuracy, tool calls, multi-step conversations and complete task completion—across models and prompt versions—so you know where and how your system falls short.
What Can Go Wrong
Quality failures we surface across real AI systems
Silent Accuracy Drift
Outputs look fluent and confident while being factually wrong, incomplete, or subtly off-task.
Incomplete Task Completion
The system appears to finish a task but skips steps, drops constraints, or stops halfway without signalling failure.
Quality Loss After Changes
A new prompt or model version quietly breaks cases that used to work, with no reusable test coverage to catch it.
Unmeasured Edge Cases
Rare but high-impact inputs are never tested, so failures only surface once real users hit them.
Our Testing Methodology
How we turn quality from an opinion into measurable evidence
Define Quality
We agree on what 'good' means for your use case—accuracy, completeness, tone, safety and task success.
Build Test Sets
We assemble representative and adversarial cases, including the edge cases that matter most.
Evaluate & Score
We run automated and human evaluation across outputs, retrieval, tools and full workflows.
Lock In Reusable Coverage
We convert confirmed failures into reproducible tests so quality holds across future changes.
What You Get
Quality Evaluation Report
A clear picture of where your system performs and where it fails, with evidence and severity.
Reproducible Test Suite
A reusable set of test cases and metrics your team can rerun on every change.
Improvement Roadmap
Prioritized, practical steps to raise quality where it matters most.
Know How Your AI Really Performs
Start with a focused quality evaluation of the outputs and workflows that matter most.
Discuss a Partner Pilot