Data Quality & Integrity Audit
Your AI tools are only as good as the data you feed them — find out exactly where your data is clean, where it's broken, and how to fix it.
When was the last time anyone checked whether the data your business runs on is actually clean?
What is the problem?
AI tools promise extraordinary leverage — but only when the underlying data is reliable. Duplicate customer records. Misformatted dates. Inconsistent product codes. Fields that are blank when they should be populated. Missing values that look fine until an AI system tries to use them and produces nonsense. Most businesses have some version of this problem. Many don't know how bad it is because nobody's ever systematically looked.
How does AI solve this?
We build an AI-powered data audit that examines your key business databases — customer records, product catalogues, transaction histories, project data — and produces a structured report on data quality issues: what's missing, what's inconsistent, what's duplicated, and what will cause problems if you try to use this data as the foundation for AI-powered workflows.
How does this work in practice?
Connect your data source
We access your database, spreadsheet, CRM export, or any other structured data store. An export is sufficient for the audit.
Run systematic quality checks
The AI examines every field against data quality rules: completeness, consistency, format conformance, duplicate detection, and referential integrity.
Categorise by severity and impact
Issues are ranked by how much they'll affect AI-powered workflows and business decisions.
Generate a remediation plan
The report includes not just what's wrong but how to fix it — specific queries, transformation rules, or manual review tasks.
Set up ongoing monitoring
Automated quality checks that run on a schedule and alert you when new issues appear.
What are the benefits for your team?
Unlock the value of AI tools you've already invested in
The most common reason AI tools underperform is poor input data. Fixing the data is the highest-leverage action.
Find problems before they cause expensive mistakes
A duplicate customer record is an inconvenience in a spreadsheet and a serious compliance issue in an AI-powered workflow.
Build confidence in your business data
Teams that trust their data make faster, better decisions.
Required foundation for any serious AI implementation
Before building AI agents or automations, you need to know the data is reliable.
Works across any data type
Customer databases, inventory systems, project management exports, financial records, HR data.
Ready to explore this for your team?
Let's have a no-obligation chat about how this could work for your business.
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