Propose a method for assessing competitor AI products without direct access to their algorithms or datasets.

Example answer

Begin with representative user tasks and an explicit scoring rubric: correct completion, supported evidence, error severity, latency, cost and workflow usability. Use authorized public/demo access consistent with each service's terms and avoid uploading private or sensitive material. Keep the same task inputs and comparable configuration, record product/version/date and repeat stochastic outputs enough to expose variability.

Blind reviewers to the vendor where practical, preserve failing examples and report per-task/slice results with sample sizes. Distinguish what was observed from what was advertised. A missing feature in a trial may reflect account permissions rather than an underlying model limitation, so record that uncertainty rather than infer internals.

Reviews, public adoption and pricing help assess commercial position and user expectations. They do not establish task accuracy, reliability or causal customer value: popularity can reflect distribution, bundling or marketing. If no lawful hands-on evaluation is available, produce a sourced feature/claims comparison and explicitly label capability performance unverified.

The resulting decision should state where each product meets the intended task, what operational constraints remain, and what evidence is missing. Do not scrape restricted areas, evade access controls or seek proprietary algorithms to make the comparison.