AI strategy & consulting
Deciding whether, where and how AI is worth applying.
- AI readiness assessment
- Use-case identification and prioritisation
- Custom adoption roadmap
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Three practices — applied AI, software engineering and resource augmentation — that we staff from one team, so nothing falls through the gaps between them.
We use AI and machine learning to solve specific business problems, improve productivity and open up work that wasn't practical before. Every engagement starts with a use case, not a technology.
Deciding whether, where and how AI is worth applying.
Building and training the models your use case actually needs.
Taking repetitive work off people who have better things to do.
Adding intelligence to the systems you already run.
Solutions shaped by the sector they run in.
The unglamorous work that makes the rest possible.
The practice the company was founded on. Full-stack product engineering across web, mobile, cloud and enterprise systems.
One of our core business lines. A network of pre-assessed AI, data, cloud and engineering specialists, deployed in one to three weeks with replacement cover written into the engagement. They join your stand-ups, use your tools and report to your leads.
Pick the commercial shape that fits the work, not the other way round.
Domain familiarity shortens discovery and reduces the cost of getting it wrong.
Describe the problem in plain language. We'll tell you what it would take, and say so if AI isn't the right tool for it.