Automated Testing
Suites that run on every change, so a break is caught in minutes, while it is still cheap to fix.
Speed and reliability are the same problem
Teams that ship quickly are not being reckless. They are the ones with tests doing the checking, which is what makes releasing on a Tuesday afternoon an ordinary thing to do.
Every change runs the suite. When something breaks, it breaks in a pull request with the change that caused it still on the screen, minutes after it was written and while the person who wrote it still remembers why. The alternative is finding out weeks later from somebody who is not on your payroll.
Coverage decisions come from a test strategy. This is the part that builds and runs them, alongside performance and load testing.
Four ways we get brought in
Most work starts with one of these, and the second is the most common.
Building a suite
From nothing, starting with the flows that carry money, data or compliance evidence.
Rescuing one nobody trusts
Slow, flaky and quietly ignored. Fixing it usually means deleting a lot of it first.
Covering a legacy system
Tests around the behaviour you cannot afford to break, written before anyone changes anything.
Wiring it into the pipeline
A suite that only runs when somebody remembers is not a safety net. It runs on every change.
Over 3,200 automated tests on our own product
This is the discipline we bring to client work, and we run it on ourselves first.
| Item | Result | Notes |
|---|---|---|
| Tests on our own product | 3,200+ | across nine test projects, including architecture tests that enforce how the codebase may be structured, and an end-to-end suite |
| They run on | Every change | not nightly, and not the week before a release |
| On our last client build | 300+ | running continuously through a system that went live in under four weeks, at a fixed scope and fixed price - a relatively simple installation |
Built in layers, on purpose
The shape of the suite comes from an assessment of your stack, your delivery model and what's realistic to maintain - not from a template.
End-to-end where risk is highest
UI tests cover the user journeys and system behaviours that carry the greatest risk, structured for reliability and speed. A slow, flaky UI suite is the one teams learn to ignore, so it stays small and it stays trusted.
API tests do the heavy lifting
Most coverage sits at the API layer: faster and more dependable than testing through the screen, without the fragility that makes UI suites expensive to maintain.
Performance in the same pipeline
Where performance is in scope, automated suites simulate real demand and find the bottlenecks before production does - the discipline behind our performance and load testing.
Common questions
Our suite is slow and half of it fails randomly. Is that fixable?
Usually, and it is one of the more common reasons we are called. A suite nobody trusts is worse than no suite, because it trains a team to ignore failures. The fix is normally deleting a good deal of it, stabilising what is left and making it fast enough that people wait for it.
Can you add tests to a system that has none?
Yes, and the order matters. We put coverage around the behaviour you cannot afford to break before touching anything, so the tests describe what the system genuinely does, quirks and all.
Who owns the tests afterwards?
You do. They live in your repository, run in your pipeline, and are written to be read by your team. A suite only one supplier can maintain is a liability dressed up as an asset.
How long before it is worth anything?
Quickly. The first tests go on the flows that carry the most risk, so a useful safety net exists well before the suite is finished.