London AI consultancy. We map your processes, score your data readiness, and hand you a prioritised plan — so you spend money on the right thing, not just something.
Fixed price. 2–4 weeks. You own everything we produce.
Let's Talk →We document and score every workflow for AI potential. Not theory — we sit with your team and map what actually happens, then rate each process against effort, value, and feasibility.
Scope and price confirmed before we start. 2–4 weeks delivery.
73% of failed AI projects had weak data foundations. Before any build, we score your data quality, accessibility, and structure against what AI actually needs — and give you a clear picture of the gaps that would kill a project if left unaddressed.
Structured. Validated. Ready for analysis.
Four weeks. One clear output. You leave with a prioritised action plan, not a 60-slide deck full of caveats.
We interview your team, shadow key workflows, and map every process against AI potential.
Data readiness scored. Use cases ranked by ROI, risk, and time-to-value. Gaps identified.
Written report. Top 3 opportunities. Cost estimates. Build roadmap. Yours to keep.
The audit is vendor-agnostic. We recommend what fits your stack and budget — not what benefits us. If we're not the right firm to build it, we'll tell you.

Fragmented data across spreadsheets and APIs. Poor data hygiene with duplicates and missing identifiers. GDPR compliance gaps.
Microsoft Fabric data pipeline with star schema, 30+ DAX measures, GDPR-aligned policies, and four-page Power BI dashboard.

Only 5 columns of data available. No context or objectives. Client brief: "Find us something useful."
Metric-first analysis with DAX-enriched data. Power BI dashboard revealing operational bottlenecks and revenue opportunities.
We use a proven framework to score processes for AI fit, assess data readiness against real AI requirements, and rank opportunities by ROI and implementation risk.
Audit your existing Microsoft AI tools for untapped value.
Which processes benefit from language model integration.
Where custom AI outperforms off-the-shelf tools.
We recommend what fits — not what we're partnered with.
73% of failed AI projects had no agreed definition of success. 68% underinvested in data before starting. If your project stalled, one of these four things happened.
Nobody agreed what “working” actually meant. The build finished. Nobody could say if it succeeded.
The AI worked in the demo. Then it hit your actual data — messy, inconsistent, inaccessible — and stalled.
62% of AI tools fail to reach 40% adoption in 6 months. Without someone owning it day-to-day, teams go back to what they know.
The audit addresses all three before anything gets built. We identify the root cause of what went wrong, assess whether the underlying opportunity is still valid, and give you a clear path forward — or tell you honestly if the idea isn't worth pursuing again.
No pressure. No sales pitch. Just honest conversation about how we can help with your data and reporting needs.
Let's talk about your project →