SixMap's engineering team ships on a bi-weekly release cycle, but their UI testing process couldn't keep pace as their development pace increased, thanks to AI coding agents. Before every release, the team spent roughly two days manually "clicking through every screen in staging" to catch regressions: a bottleneck that limited how fast they could ship.
Much of SixMap's UI risk lives in agreements between views rather than on any single page. A CVE count on the vulnerabilities grid has to match the CVE detail page. An IP count in inventory has to reconcile against the scoped detail view. Sorting and filtering have to hold across grids backed by server-side pagination.
These are the failures that matter most to customers and the ones least suited to manual QA: checking them by hand means holding numbers from one screen in your head while navigating to another.
The team had already tried traditional end-to-end tests, but they proved too brittle for a product that changes as fast as SixMap's. Small UI tweaks constantly broke existing test scripts, creating more maintenance work than the tests saved.
On top of the technical challenge, SixMap was evaluating this as a startup. An enterprise competitor quoted roughly $100k/year for a comparable testing solution: a non-starter at their stage.
SixMap brought in Cofactor, an AI-driven end-to-end testing platform, to automate its UI regression suite.
Cofactor's AI agent builds and runs tests by navigating the product the way a real user would, rather than relying on brittle, hard-coded selectors. When the UI changes, Cofactor's multi-layered fallback system lets tests self-heal instead of breaking outright, cutting the maintenance burden that had made SixMap's earlier attempt at automation unsustainable.
AI-driven test creation via Cofactor CLI using natural-language instructions, Jira tickets, and existing manual test cases
Self-healing tests that adapt automatically to UI changes instead of breaking on minor tweaks
Parameterized, multi-tenant test templates for validating roll-up data consistency at scale
Role-based access testing, using multiple credential sets to validate different permission levels (e.g., read-only vs. admin)
High-signal failure reports: every failure contains video, logs, and repro steps and a diagnostic — delivered straight to Slack, including a ready-to-use prompt for fixing the underlying issue
Cofactor quickly became a core part of SixMap's release process. What started as a way to fill a specific testing gap grew into a critical QA agent embedded directly in SixMap's deployment pipeline.
By replacing manual UI regression testing with self-healing, AI-driven tests, SixMap was able to speed up their release process, cut down on time spent manually testing the product and ultimately ship with confidence.
“We used to lose almost two full days before every release to manual testing in staging. Now that time goes back into building products for our customers.”