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Per-run results

Every persona execution is a test run with its own detailed record:
  • Conversational tests — a full interview transcript per screen (moderator and persona turns), alongside the screen image.
  • Expert audits — heuristic-by-heuristic ratings (Good / Fair / Poor / Not Applicable) with the specific issues found.
  • Website tests — the step-by-step action log with screenshots and reasoning, task completion status with a confidence score, abandonment reason if the persona gave up, and the session video replay.
  • SUS score (if enabled) — the persona’s questionnaire answers and computed 0–100 score.
  • Run summary — key findings plus recommendations prioritized as P0 (critical), P1 (important), P2 (nice to have).
  • Accessibility analysis (website tests) — WCAG-based compliance check with an impact breakdown.
Runs progress from running to completed (or failed — a failed run can be retried once, free unless the retry succeeds; prototype runs may be partially successful if only some screens complete).

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Aggregated test summary

Above individual runs, each test gets an aggregated summary across all personas and executions:
  • Test overview and an overall results rating — Excellent, Good, Fair, Poor, or Critical — with an explanation.
  • Key takeaways, each tagged with sentiment (positive / negative / neutral).
  • Prioritized recommendations, linked to the pages or screens they concern and to the supporting runs and quotes — so every recommendation has receipts.
Summaries generate on demand and can be regenerated after you add more runs.

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Sharing & feedback

  • Public result links — share a read-only view of a test with anyone, no Versive account needed; revocable at any time.
  • Word & Markdown export — formatted reports for stakeholders.
  • Rate the run — leave a 1–5 star rating and comment on any run; this feedback goes straight to the Versive team.

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Reading AI test results well

  • Treat findings as hypotheses ranked by likelihood, not verdicts. AI tests are exceptional at surfacing where flows confuse and what to look at; confirm the highest-stakes findings with a small real-user study.
  • Look for repetition across personas and executions. An issue that appears in four of five runs is signal; a one-off is a maybe.
  • Treat priorities as guidelines. The P0/P1/P2 rankings are the AI’s read on severity — read each recommendation and decide whether it matches your own understanding before acting on it.
  • Read the abandonment reasons. Where and why the AI gave up on a task is often the single most actionable data point in a website test.