Each release costs hours or days of clicking through the product, and bugs still slip through.
Nobody ever automated, so the team deploys less often than it would like to.
You automated two years ago, it went flaky, nobody maintained it, and today it is switched off in CI. This is the most common case, and the suite did not fail for lack of skill: maintenance never fit anyone's job.
And the pressure is now going the other way. Tricentis found in June 2026 that 60% of organizations still ship untested code to production, and 30% of them say the volume of AI-generated code has grown too large for their teams to test fully. In a separate June 2026 study, Info-Tech Research Group found that 67% of practitioners agree AI-generated code requires more testing than human-written code.
Every product change breaks part of a suite, and someone has to diagnose and repair it. That labor is what made suites expensive to sustain, and it is the labor AI absorbs well. But an agent repairing its own tests unsupervised is how a suite stops testing anything, because the fastest way to make a test pass is to make it check less.
So nothing the agent writes reaches your suite unreviewed, and the review happens twice.
Every change the agent proposes is reviewed by a second model that did not write it, looking for one specific thing: a test made to pass by verifying less. Assertions quietly loosened, steps skipped, validations emptied, expected values replaced with whatever the code returned. If the change looks like that, it is blocked rather than merged.
Our engineers define the expected behavior and the values it must produce, calculated independently of the screen being verified, and sign off on what lands.
We walk your product with you and write down the flows that matter. The output is a coverage map: which flows get protected, which do not, and why.
The suite gets written against those flows, with expected values calculated independently rather than read off the screen under test.
It runs in your CI, on your repository, on every change, with evidence attached to every run.
The product changes, the suite follows: AI repairs, an engineer approves.
The suite, in Playwright and TypeScript, in your repository. Code you can read, run and keep.
Running in your CI, on every change, on your infrastructure.
A navigable report of every run, with evidence attached.
A coverage map against your flows: which are protected, by which test, asserting which behavior.
Declared exclusions with a manual plan: what we do not automate, why, and what it costs to check by hand.
Done means: the flows on the coverage map pass in your CI on every change, and every exclusion is listed with the manual time it takes.
Playwright with TypeScript by default. Cypress, Selenium or WebdriverIO if that is where your suite already lives.
If you have no preference, we write in Playwright with TypeScript. It is the de facto standard now, and it gives us parallelism without a grid, auto-waiting that removes a class of flakiness, and real Safari coverage. We tell you what we picked and why.
If your suite is in Cypress, Selenium or WebdriverIO, we continue there. We will not ask you to migrate as a condition for working together.
End-to-end web. Full user journeys through a real browser.
API. Covered inside the same suite, with the same fixtures and authentication, so there is no second framework to maintain.
Your CI. GitHub Actions, GitLab CI, Jenkins, Azure Pipelines, CircleCI.
Accessibility checks in the suite. Automated rules run alongside the functional tests. Automated checks catch roughly a third to a half of accessibility issues, so this finds the detectable ones and does not replace a manual audit.
Unit and integration tests stay with your team. We integrate with what already runs and leave it as it is. And in any case, the result is readable code in your repository, under your version control, running in your pipeline. If you stop working with us, the suite still runs.
Some checks are not worth automating: judgment calls, one-off visual reviews, and cases where reproducing the conditions costs more than checking by hand. We list which ones, why, and how long they take manually.
Everything runs in your infrastructure: your repository, your CI, your environments.
Least access, and it ends when the engagement does.
Secrets stay out of the repository, through your secret manager and environment variables, and run evidence is checked so it does not leak them.
The first job is diagnosis. Some suites need stabilizing and some need regenerating, and we will tell you which one yours is before anyone touches it.
You do. They are code in your repository from the first commit.
No. The suite runs where your pipeline already runs.
The suite follows: AI repairs against the defined behavior, an engineer approves the change.
No, and we will say which parts and why. What cannot be automated well gets a manual plan with the times they take.
One working session: we walk the flows that matter in the part of your product that hurts most, and you leave with the coverage map and the plan.
Everything the suite learns about your system stays as an engineering brain you keep. If you later grow a Company Brain, this is already a module of it.
Learn about the Brain →