Turn a data file into decision evidence.

A focused, human-validated diagnostic completed over two weeks. We profile the selected data, test its quality and relationships against the agreed use case, review material findings with accountable people and establish an evidence-based baseline rather than relying on an automated score alone.

The engagement examines up to three priority structured sources — such as a CRM export, finance table or operational spreadsheet — so the scope remains controlled and useful. We assess identifiers, definitions, completeness, duplicates, consistency and cross-source joins to reveal the issues that matter most.

We use only the minimum data necessary for the agreed purpose, preferably through a minimised and non-sensitive extract. Sensitive fields can be excluded or masked, access stays limited to authorised participants and every proposed use is considered against purpose, protection and retention requirements.

Findings are converted into a practical 30–60–90 day improvement plan with prioritised actions, named owners, dependencies, evidence checks and measurable outcomes. It shows what to correct first, what controls to establish and what must be proven before the selected AI use case can progress responsibly.