Quality Engineering That Protects Your Reputation

No, and teams that try usually end up with a slow, flaky suite nobody trusts — which is worse than a small one people believe. Automate what is stable, high-risk and repetitive: authentication, checkout, permissions, the paths that would cost you money if they broke overnight. Leave brand-new features, one-off flows and anything still changing shape to manual testing until the design settles. A good rule is that a test which fails for reasons other than a real bug has stopped being an asset, and either gets fixed that week or deleted.

Developers test that the code does what they intended. QA tests what happens when someone does something else. That is a genuinely different activity, and it is hard to do against your own work because you already know the intended path. A dedicated tester brings the awkward inputs, the interrupted flows, the stale session, the slow network and the device nobody owns. Developer testing and QA are complements — we would not recommend replacing unit tests with manual QA any more than the reverse.

Enough that you can release without a meeting about whether it is safe to release. Coverage percentages are a poor target on their own — a codebase at 90% can still have every critical path untested. We scope from risk instead: list what would actually hurt if it broke (payments, auth, data loss, anything regulated), make sure those have automated coverage that runs on every commit, and accept thinner coverage on the parts where a bug is an inconvenience. That conversation takes an hour and is worth more than any coverage number.

Not with the same tools, no. Conventional tests assert that a given input produces an exact output, and a model-backed feature does not work that way — it fails quietly and plausibly rather than throwing an error, so error rates stay flat while quality drops. Those features need an evaluation set: stored inputs, a definition of a good response, and a score you can compare across changes. We build both, because the deterministic 80% of an AI product still needs the testing it always needed.