A plan your board and your engineers both trust
The output is a ranked list of use cases, a roadmap with budget ranges, and a pilot defined tightly enough to start building.
Process review
Workshops with the people doing the work. We map hours, handoffs, error rates and the systems each step touches.
Use-case ranking
Each candidate is scored on business value, data readiness, integration effort and risk, so the first pick is defensible.
Data readiness check
What data exists, where it lives, how clean it is, and which gaps must be fixed before a pilot.
AI roadmap
A sequenced plan from pilot to production to scale, with budget ranges, owners and the metric each stage moves.
Pilot plan
Scope, data, success metrics and a go or no-go rule, agreed before the first line of code.
Governance
Where people review AI output, how data is protected, and what is logged for audit and compliance.
From a list of ideas to one pilot worth building
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Discover
Interviews with each team and a review of the systems and data behind their work.
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Score
Rank use cases on value, readiness and risk; agree the shortlist.
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Plan
Roadmap, budget ranges and the business case for the first use case.
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Pilot
Prove the first use case on your real data and measure it.
Questions we hear most
Still unsure? Ask an engineer.
A structured plan that connects AI initiatives to business goals, data readiness and a realistic path to production, so you move from isolated pilots to systems you can measure and trust.
No. The data behind your first use cases needs to be organised and reliable. We find the gaps early and fix the ones that matter before the pilot.
On the workflow, not the model: time saved, handling cost, response time, manual effort removed and service quality, all agreed before the pilot starts.
One that solves a clear business problem, moves a metric you already track, and fits your existing data and workflows.