Get your data ready for AI. We audit what you have, fix what's broken, and build foundations that support accurate, reliable AI applications.
AI is only as good as the data behind it. If your information is scattered, inconsistent, or poorly managed, AI projects will struggle regardless of how clever the technology is. We help you understand what you have, fix what needs fixing, and build data foundations that actually support AI success.
Every AI application relies on data: to train models, to answer questions, to make decisions. When that data is incomplete, outdated, or contradictory, the AI inherits those problems. Users lose trust. Projects fail.
The organisations that succeed with AI usually have sorted their data first. Not perfectly, but well enough that AI systems can do useful work.
Our audit examines your data across several dimensions.
Quality assessment measures accuracy, completeness, consistency, and timeliness. We sample data from key systems and quantify issues. You will know exactly where problems exist and how severe they are.
Architecture review maps how data flows through your organisation. Where does it originate? How does it move between systems? Where are the gaps and duplications? This reveals structural issues that cause ongoing problems.
Governance evaluation looks at how data is managed, protected, and controlled. Who owns it? What policies exist? How are changes tracked? Good governance prevents many quality issues before they start.
AI readiness scoring assesses whether specific datasets can support planned AI applications. Some data needs significant work; other data is ready now.
Audits tell you where you are. Strategy tells you where to go. We help organisations create data strategies that align with AI ambitions while remaining realistic about what can be achieved.
A data strategy addresses:
Priority datasets and improvements. Which datasets matter most for your goals, and what changes would make them usable.
Governance that fits your organisation. Clear ownership, access controls, and change management without bureaucracy for its own sake.
Technology and integration needs. What needs to exist for reliable access, lineage, and consistency across systems.
Skills and roles. What needs to be owned internally, what can be supported externally, and how to avoid “orphaned” data work.
A practical roadmap. Milestones, dependencies, and sequencing that aligns with delivery constraints.
Depending on scope, our data work delivers:
A data audit report. Quality scores, issue inventory, and clear examples of what is breaking trust.
Architecture documentation. Data flows, dependencies, duplication points, and where problems are introduced.
AI readiness view by dataset. Which datasets are usable today, which need work, and what “good enough” looks like.
Prioritised recommendations. What to fix first, what to defer, and why.
An implementation plan. A roadmap with sequencing, dependencies, and realistic resourcing guidance.
Data work pays dividends whenever AI is on the agenda. Starting early avoids delays later. If AI projects are already underway and struggling, data is often the root cause worth investigating.
We also help organisations preparing for regulatory requirements around data governance, or those planning major system migrations where data quality matters.
We work pragmatically. Perfect data is neither achievable nor necessary. Our focus is on making data good enough to support your specific AI goals, not on abstract ideals.
Use these prompts to clarify what “good enough data” means for your first AI initiatives.
“Which datasets do we need for our first AI use case, and what quality issues would undermine outcomes?”
“What governance controls are necessary for safe, compliant data access without slowing delivery?”
“How should we prioritise data improvements so we unlock one valuable use case quickly?”
A structured review of your data landscape—sources, quality, governance, and flows—plus a practical plan for improvements aligned to your AI goals.
No. Good data foundations also improve reporting, operational efficiency, and decision-making. AI simply makes weaknesses show up faster.
No. The goal is to identify which datasets and improvements matter for the first use cases, and sequence the work accordingly.
Clear ownership, access rules, and change control so people can trust data and use it safely—without creating unnecessary bureaucracy.
You can move into targeted remediation, use case discovery, or feasibility work—using the audit findings to reduce risk and speed up delivery.