Cleaning data before an AI project does not mean making every database perfect. That would take forever and usually never happen. It means cleaning the slice of data the AI system actually needs to answer the business question.
Start with the use case
If the AI system will answer policy questions, focus on policies, ownership, version control, and permissions. If it will support sales, focus on accounts, contacts, case studies, product docs, and proposal history. The use case decides the cleanup.
The cleanup checklist
- Choose the sources that count.
- Remove or quarantine stale documents.
- Match duplicate customers, suppliers, or entities.
- Standardize key fields and definitions.
- Set access rules before retrieval starts.
- Create review paths for low-confidence answers.
- Document who owns each source.
This work feels slower than building the interface. It is faster than launching a tool nobody trusts.
AI readiness is not a purity test. It is about making the specific data behind the use case reliable enough to use.
Lucendata helps companies clean and structure the operational data needed for practical AI systems.