Automation, at the scale
the traffic actually arrives

Automation is easy at ten users.
It is a different craft at scale.

A suite that passes on a developer machine tells you almost nothing. The engineering problem is holding correctness while volume climbs — and having the instrumentation to know which of the two just broke.

On a connected-vehicle fleet platform covering 50,000 vehicles, we validated the system at 56,000 requests per minute across baseline, spike, capacity, stress and endurance profiles. On a core banking platform, 2 million transactions per test cycle and peak profiles reaching 5.4 million transactions per hour.

Those numbers are not trophies. They are the reason we can tell you where your platform bends before your customers find it.

Ramp, plateau and soak — the shape of an honest load test

We use AI where it earns
its place, and audit it where it does not.

On a live e-commerce replatform, AI-assisted scenario generation accelerated coverage by 40% across complex user journeys. That is a real, measured result from a real engagement.

It is also a capability that needs supervision. Generated scenarios drift toward the obvious cases and quietly skip the ugly ones — the partial refund, the expired token mid-checkout, the fulfilment split that strands a line item. We review every generated case against the risk model before it enters the suite.

We are standardised on Anthropic’s Claude for this work, and we work inside a client’s own model estate where they mandate one. Either way the governance is identical: mandatory human review, no client production data into a model, and no model anywhere in the test execution path.

Our principal's doctoral research is in machine learning applied to software testing, with post-graduate AI and ML study at Caltech. We are enthusiastic about the tooling and sceptical about the claims, in roughly that order.

Generation is a step in the process, never the whole process