AI Testing & Design

AI test automation uses AI models and agents to generate, maintain, execute and triage tests — including test generation from requirements, self-healing selectors, risk-based test selection and automated failure analysis.

No. AI removes a large share of the repetitive work — writing boilerplate tests, maintaining selectors, triaging flake — but deciding what's worth testing, judging business risk and exploring a product intelligently remain human work.

Workflows in which several specialised AI agents divide the job: one explores the application and maps its flows, another plans coverage against risk, another writes the automation, and another executes and reports. A QA engineer reviews and owns the output.

Yes. We implement the tooling, integrate it with your pipeline and repositories, define the guardrails and review process, and train your team to run it — then hand it over.

It depends entirely on configuration. We agree data-handling boundaries up front — which models, what retention, what leaves your environment — and can implement workflows that keep code and data inside your own infrastructure.

The consistent gains we see are in automation authoring and maintenance rather than execution — often a substantial reduction in the time spent writing and repairing tests. Real figures depend on your stack, so we baseline before claiming anything.