StepWise Lab evaluates how well AI models understand video. Each model's response is compared step-by-step against expert ground truth, producing quantitative breakdowns of where and how reasoning fails.
Each response is scored across multiple dimensions and checked for specific failure types — not reduced to a single accuracy number.
Each model's response is compared side-by-side against expert-authored ground truth for the same video, capturing differences at every step.
When models get it wrong, we don't just mark it as "incorrect" — we categorise the failure type: hallucination, self-contradiction, temporal displacement, omission, and more.
AI generates draft failure analyses that reviewers can accept, override, or refine. This keeps expert review thorough while reducing the time spent on each video.
Models are scored across dimensions like action sequence accuracy and counterfactual inference — revealing specific strengths and weaknesses rather than one aggregate score.
Every review session produces structured, queryable data you can export and analyse.
Standard benchmarks give a single score. StepWise reveals what failed, why, and how often.
Unlike outcome-only scoring, StepWise Lab lets you inspect how a multimodal model arrived at an answer. This makes it possible to identify step-by-step reasoning errors that can lead to inconsistent outcomes as task complexity increases.
Automated benchmarks miss subtle errors in video understanding. StepWise Lab supports expert review with an AI-assisted workflow, so feedback stays high-quality without slowing evaluation.
StepWise Lab uses consistent failure categories compiled from published research and refined for practical evaluation. This helps ensure reasoning failures are identified consistently across reviewers and evaluations.
When something goes wrong, people turn to video to answer one basic question: what actually happened? In security contexts, AI is increasingly expected to help by interpreting footage and summarising events over time. But when it misreads the sequence of events or invents details, the outcome can be missed incidents, wasted investigation time, and false alarms that reduce trust.
What was missing is a reliable way to measure how video reasoning fails. A single score doesn't tell you whether the issue was timing, visual grounding, or causal reasoning — and without that, improving reliability becomes guesswork.
StepWise Lab provides a human-in-the-loop evaluation workbench that standardises error identification and turns qualitative review into measurable signals and quantitative error breakdowns across models and evaluations.
Whether you're working on video AI, evaluation methodology, or model reliability — I'd love to connect.