AI
Strategy

Start with the free AI readiness assessment on our site, which takes a few minutes and gives you a structured read on data, process maturity and likely use cases. We use your result as the opening agenda for a scoping call rather than starting from a blank page.

In a working session with the people who do the job, we collect candidates from where the repetitive work actually is: questions answered the same way every day, documents somebody reads and summarises, data re-typed between systems. Every candidate is written as a task with a volume, an owner and a way to tell whether it was done well.

For each shortlisted use case we trace the data it depends on — which system holds it, how current it is, how consistent the formats are, and which fields must never be sent to an external provider. This is where most AI projects actually stall, so we report it plainly rather than assuming it can be sorted out later.

Candidates are scored on business value, data availability, tolerance for a wrong answer and how measurable the outcome is. For the survivors we compare three routes honestly: an existing product you subscribe to, an integration on a hosted model API, or custom development — with the total cost of each over the first two years.

The recommended use case gets a written pilot spec: scope, evaluation questions, what counts as success, and the conditions under which we would stop. Alongside it comes a cost model that includes ongoing token or inference spend at your real volume, plus the human review time that a responsible rollout still needs.

You receive documented data flows, retention and provider settings, refusal and escalation rules, and where a human stays in the loop for consequential decisions. The roadmap closes the engagement by separating what to start now, what to revisit in six months and what to drop entirely.