Scale is where a single working system becomes capability your whole business can rely on. We roll proven systems out more widely, give them more responsibility as they earn it, and reuse what we've built so each new use case costs less than the last. All the while we keep quality, cost and drift in check, so the results hold as usage grows.
Scaling isn't just more users. A proven system can reach more of the business, take on more of the work, or open the door to the next idea. We help you pick the move with the best return, rather than all three at once.
Roll the system out to more teams, sites, brands or customer groups, adjusting for how each one really works.
Move from AI-assisted towards AI-augmented or AI-powered, one step at a time, as the evidence supports it.
Go back to the ideas parked during Assess. With the foundations already in place, the second use case comes in faster.
The first use case pays for the foundations. After that, each new one reuses them, so less of the effort goes on groundwork and more goes on the new idea itself.
| Use case | New work | Reused |
|---|---|---|
| Use case 1 | 100% | 0% |
| Use case 2 | 65% | 35% |
| Use case 3 | 50% | 50% |
| Use case 4 | 40% | 60% |
AI systems don't stay still. Your data changes, your customers change, and the models underneath change. These are the four signals we track, and what we do when one moves.
Is it still as accurate as at go-live?
Has the data or behaviour shifted?
What does each task cost to run?
Are people using it and trusting it?
Illustrative trends · shaded band shows the agreed range
When a better or cheaper model is released, or a provider retires one, we don't just swap it in. Every change goes through the same cycle, checked against your own baseline, and only ships if it wins.
Live quality, cost and usage compared with the agreed baseline
Drift, new failure modes, cheaper models, or a new need from your users
Through the Code and Data Factories, on its own branch, with the full checks
Against the evaluation set, so nothing that worked before gets worse
Approved by an engineer, rolled out in stages, and recorded
Some businesses want us to run everything. Others want to build their own capability and keep us on call. Most move along the scale over time, and we plan for that from the start.
We run, monitor and improve the system for you, and report back on value and cost.
Your team owns the day-to-day. We handle improvements, model changes and anything unusual.
Your team runs and extends it. We stay on call, and come back in for the next big step.
We track four signals continuously: quality against the go-live baseline, drift in data or behaviour, cost per task, and adoption. Each has an agreed range and a planned response when it moves.
Only when it's worth it. Every change is tested against your own evaluation set and released only if it wins, with an engineer approving the release.
Yes. We offer managed, co-run and handover models, and we train your people and document everything so you can move between them.
Usually, because data pipelines, connectors, test sets, guardrails and governance paperwork are reused. How much depends on how closely the use cases are related.
Everything learned in production goes back into the next round: which ideas worked, what your data can support, and what your people now trust AI to do. The second Assess is quicker and sharper than the first, because the evidence is real.