Problems We Solve
Most businesses assume their tests are protecting production because they pass. These are the problems that assumption hides, and how Obvyr replaces it with evidence.
Why do my tests fail randomly in CI?
Tests pass, then fail, then pass again on retry, and pretty soon nobody trusts the result either way. Obvyr automatically detects and ranks flaky tests, builds a health profile for every test from its full pass/fail history, and rolls it up into a project-level flakiness rate, so you know which failures are real and which are noise.
Which environment did that test actually run in?
A suite spread across a dozen laptops and CI runners shouldn't be a mystery. Every execution Obvyr captures is tied to the registered agent that ran it, a user or context identifier, and environment tags, right down to the individual test, with daily unique-machine and user metrics, filterable by agent, tag, and date.
Which of your hundreds of tests actually matter?
A 45-minute suite that half the team skips locally is a sign, not a strategy. Test health profiles and slowness rankings show exactly which tests are earning their keep and which are quietly inflating CI time, so you can prioritise what to fix.
How do you review AI-generated tests at scale?
AI tooling can 10x your code and test output, but reviewing whether that output is any good is still a manual bottleneck for most teams. Obvyr applies the same flakiness and health-profile pattern recognition continuously across your entire test history, turning slow manual review into an ongoing, evidence-based signal.
How do you report test quality to leadership?
"I think so?" isn't evidence. The project dashboard, portfolio view, time-series trend charts, and PDF export give you dated, auditable proof of quality over time, including a monthly and quarterly report generated automatically for every project, no one needs to remember to run.
How do you compare test health across projects?
Different teams, different pipelines, different definitions of "87% pass rate". Until now: tag-based filtering and the portfolio dashboard bring every project's headline metrics into one place, using the same definitions throughout, so anyone scanning the estate sees consistent, comparable numbers.
From assumption to evidence
Before Obvyr
- Deploy with crossed fingers
- Debug production issues that passed all tests
- Maintain test suites without knowing their value
- Manually investigate every flaky failure
- Leadership has no visibility into testing health
With Obvyr
- Deploy with evidence, not hope
- Know which tests are reliable before you ship
- See which tests protect production and which are noise
- Spot flakiness patterns automatically
- Give everyone across the business real visibility