Every failed test
has a story. Discover it.
inverseStory turns automation reports, execution logs and engineering events into visual stories — so teams understand why software broke, not just what broke.
- 10x
- faster RCA
- 94%
- flake detection
- 12+
- frameworks
checkout-web failure rate jumped 1.8% → 12.4% within 6 min of release a3f12b9.
Built for teams shipping with
Test reports give you logs. Not understanding.
Modern engineering teams generate gigabytes of test data every week. Almost none of it becomes insight.
Reports without answers
Allure, Cucumber and Extent show what failed. They never explain why or when it started drifting.
Flake fatigue
Re-runs hide real failures. Engineers waste hours separating noise from signal.
No engineering memory
Each run lives in isolation. Nobody can answer 'has this failed before?' without grepping CI.
From raw report to engineering story in seconds.
Upload
Drop Playwright, Cypress, JUnit, Cucumber, Allure or custom JSON. We parse it instantly.
Connect
We stitch runs into a continuous timeline keyed by suite, branch and commit.
Analyze
AI detects regressions, flake patterns, slowdowns and root-cause candidates.
Explore
Interactive dashboards, heatmaps and story graphs replace static HTML reports.
An engineering terminal, not a status page.
Density and clarity built for engineers who live in CI, logs and incident timelines.
Auth flakiness traces to token clock skew
12 of the 27 flaky runs in auth-service share a sub-second 401 immediately after refresh. Likely 30s clock drift in the staging container.
checkout-web failure spike correlates with deploy 8821
Failure rate jumped from 1.8% → 12.4% within 6 minutes of release a3f12b9. 9 of 12 failures hit the same /promo endpoint.
Every failure becomes a narrative.
Deploys, API shifts, test failures and AI insights stitched into a single timeline.
- T-08mdeployDeployment a3f12b9 to staging
- T-07mapi/promo response time 110ms → 1240ms
- T-06mfailcheckout-web · promo flow failed (×4)
- T-05mfailcheckout-web · cart total mismatch (×8)
- T-04maiAI: regression detected — likely cause /promo serializer
- T-02mfixRollback to be12f08 queued
See when your suites break before your customers do.
Drop any report. We speak the format.
Built for engineers. Loved by leaders.
10× faster root cause analysis
Skip log diving. Get a ranked list of likely causes within seconds of upload.
Flake detection that actually works
Statistical models separate genuine regressions from noise across runs.
Historical intelligence
Trends, drifts and seasonality — across releases, branches and teams.
Narratives, not dashboards
Every incident gets a story you can share in Slack, Linear or with a CTO.
What engineering leaders say.
We used to spend the first 40 minutes of every incident reading logs. Now we open the story timeline and we already know.
It's the first time engineering, QA and management look at the same report and agree on what to do next.
Flake noise dropped 60% in three weeks. We finally trust the green build again.
Simple, value-aligned pricing.
Questions, answered.
Which test frameworks do you support?+
Playwright, Cypress, Cucumber, JUnit, TestNG, Pytest, Robot, Selenium, Allure, Extent and custom JSON/XML. New formats ship monthly.
Do I need to change my CI?+
No. inverseStory ingests reports through upload, CLI or a single HTTP endpoint — drop it into any pipeline.
How does the AI work?+
Each ingested execution is enriched with deploy and event context, then analyzed by models trained on engineering failure patterns.
Is my test data private?+
Yes. Reports are tenant-isolated, encrypted at rest, and never used for training across customers.
Your reports already have the answers.
inverseStory finds them.
Start free. Upload your first report and see the story behind your test suite in under 60 seconds.