Test it before you bet on it.
Ideas earn their scale in the real world, not in the business case. We design and run experiments, trials and pilots that generate evidence within the constraints of governance, accountability and public scrutiny, so what reaches the market is already shown to work.
Test. Learn. Grow.
Before anything gets piloted, we work out what actually deserves one: a wide field of ideas narrowed to the few worth the time and money of a proper real-world test. Then we are deliberate about what we are running. An experiment tests whether an idea works; a pilot tests whether the delivery model holds at scale. The two get used interchangeably, and they shouldn’t be.
Experiments come first, with hypotheses, success measures and decision points agreed before anything starts. A pilot only follows when the evidence supports it, designed with the exit in mind. And the output is evidence a leadership team can act on: what worked, what didn’t, and what would need to be true at scale, with the conviction to scale, adapt or stop.
From hunch to evidence.
Experiment scoping
A wide field of ideas narrowed to the few worth the time and money of a real-world test. Riskiest assumptions surfaced, hypotheses framed, success measures agreed before anything starts.
Prototype testing
Ideas made concrete and put in front of real users early. Structured testing loops that tell you in weeks what a pilot would tell you in months.
Trials and behavioural experiments
A/B tests, service trials and behavioural interventions, designed so the result is decision-grade evidence rather than an interesting anecdote.
Pilots with an exit
A pilot tests whether the delivery model holds at scale, and we design them with the exit in mind: sunset criteria, kill-switch conditions, and the evidence to defend the decision in either direction.
AI sandboxes
New AI use-cases tested in controlled conditions before budgets commit: explicit evaluation criteria, human-review checkpoints, and an honest account of what success, failure and harm would each look like.
Test-and-learn, the government way
Our experimentation practice aligns with HM Treasury’s Magenta Book and its test-and-learn approach, so the evidence you generate stands up in the rooms where spending decisions are made.
From quick tests to national pilots.
We have designed and run experiments at every scale: lightweight tests that answer a question in days, structured trials inside major programmes, and pilots that shaped national delivery. Running through all of it is a thread of responsible AI innovation: testing not just whether AI works, but whether it is appropriate for the context it will live in, with the governance, guardrails and human judgement to use it responsibly, or the evidence to say no.
