> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getversive.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Results & reports

> What AI test runs produce and how to read them

## 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).

## 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.

## 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.

## 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](/studies/overview).
* **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.