AI & data
Prepare business data before introducing AI
Contradictory records, outdated procedures and unclear permissions can undermine useful AI. Begin with a defined task and a manageable information scope rather than copying everything into a new platform.
Connect sources to a specific task
Summarisation, procedure answers and request classification need different information. Identify what is actually relevant before collecting everything.
Work with domain staff on a representative sample including incomplete and exceptional cases. This reveals important issues without requiring an immediate full migration.
Agree meanings and authoritative sources
Different systems may use separate customer identifiers or different meanings of completed status. Define terms and stable references before automatic matching.
Assign authority and correction responsibility for important values. The model should not choose which contradictory commercial procedure is official.
Inspect records and extracted documents
Find missing values, inconsistencies and duplicates, with explicit correction, exclusion or review rules. Automatic merging can wrongly combine distinct records.
Check extraction and versions for scans, tables and text. Evaluate what the system receives rather than only the original document’s appearance.
Prepare permissions and sensitive-information handling
Classify sources by audience and sensitivity. Define permitted profiles and destinations, enforcing access before retrieval and generation.
Include responsible data-protection contacts where personal information is involved. Limit use and retention to the actual arrangement rather than assuming a generic template fits.
Maintain updates and reference tests
Define how creation, correction and removal propagate. Track processing state so obsolete or failed updates can be identified.
Keep representative tasks and expected information for later changes. Assign review and integration maintenance: reliable data is an ongoing process.
Your checklist
- Link sources to a defined use.
- Agree terms, identifiers and owners.
- Check duplicates, extraction and versions.
- Apply permissions before AI use.
- Plan updates, removal and tests.
Frequently asked questions
Is a large collection needed to begin?
Not necessarily. A limited, reliable corpus can validate an initial task; useful volume depends on that task.
Can a model clean everything automatically?
It can propose changes, but important corrections and meaning need review. Semantic inconsistencies are not always resolved through rewriting.
A project to explore
Pare-Brise Éco
Pare-Brise Éco connects cases, documents and integrations, illustrating the relationships to clarify before using information in AI assistance.
Explore the project