AI Strategy & Discovery
Most AI initiatives stall between the proof of concept and production, because the strategy was written around the technology instead of the business. We run structured discovery to find your highest-leverage opportunities, test whether your data can actually support them, and hand you a plan you can fund — so the first thing you build is not the thing you later regret.
Know where AI pays before you spend on building it.
An opportunity assessment is not a deck of possibilities. It is a ranked, time-boxed plan tied to revenue, cost, or customer-experience outcomes you can defend to a board. We audit the data estate, map the decisions that are currently slow or inconsistent, and score each candidate on value, data readiness, and what it costs you if it fails. Some of what we hand back will be a recommendation not to build, and that finding is usually worth more than the ones that say yes.
AI Opportunity Assessment
A structured audit of your processes, data assets, and competitive position to surface the strongest candidates by value, feasibility, and time to impact. The output is a ranked register your team can act on, not a workshop everyone enjoyed.
Data Readiness Review
A model is a function of its data. We assess completeness, quality, labelling gaps, lineage, and governance, so you learn what needs fixing before engineering begins rather than three sprints into it.
AI Roadmap and Architecture Blueprint
A phased build plan with technology choices, team requirements, cost envelopes, and a success metric per initiative. Each phase is sized so that failing it is survivable and informative.
- AI opportunity register, ranked by value × feasibility
- Data estate audit + remediation priorities
- Phased AI roadmap with budget and time-to-value estimates
- Technology stack recommendation with the trade-offs stated
- Success metrics framework and measurement plan
The engagement
Discovery
Stakeholder interviews, process mapping, and a data audit across the business units where the volume and the friction actually live.
Score
Rate every candidate on value, data readiness, implementation complexity, and the blast radius if the model is confidently wrong.
Sequence
Order the initiatives so that early ones are cheap, reversible, and produce the data the later ones will need.
Handoff
Present the findings, agree the priorities, and brief the engineering team — ours or yours — on the architecture decisions behind them.
Common questions
Typically 3–6 weeks. Two weeks of discovery, one of analysis, and a final week building and presenting the roadmap. Organisations with many business units or fragmented data take longer, and we will say so before we start rather than after.
No. The Data Readiness Review exists precisely to tell you what data capability you need to build. Many clients come to us because they do not know where to start, which is a perfectly reasonable place to begin.
Then that is the answer, and you will get it in writing with the reasoning. Telling a client to spend six months fixing their data before touching a model costs us a build engagement and saves them a failed one.