> Cleaning the data.
An AI plugged into wrong data produces wrong answers, faster. Cleaning is not an optional first step.
_Your situation
_You probably recognise yourself here
- The same client exists under three spellings in three tools.
- The free-text fields hold the useful information, and nobody reads them.
- Your reference data differs from one tool to the next, with no mapping table.
- You dare not plug the AI into a base you doubt.
_Our answer
_Cleansing & connecting data sources to AI
No AI makes up for wrong data. It spreads it.
A quality audit item by item, deduplication, field normalisation and unified reference data. Then the connection to the models, once the base is sound.
_What we do for our clients
- Quality audit: duplicates, empty fields, inconsistencies
- Deduplication and merging of records
- Field normalisation and unified reference data
- A mapping table between your tools
- Sound sources connected to the models
- Update rules, so the base does not decay again
_Our proofs
_Figures that speak
“We found out a quarter of our contacts existed twice. So the AI answered us twice.”
_The tools involved
_The tools you will have access to
Anthropic - Claude
Document analysis and business automation.
OpenAI - ChatGPT
Conversational assistants and content generation.
Mistral
The French models, to keep your data in Europe.
Arthur
AI by SommeSi, plugged into your tools and your data.
Open Source models & local hosting
Open models hosted on your own infrastructure.
Notion
The knowledge base the AI reads from.
> Did you recognise yourself?
Twenty minutes to understand your situation and tell you what we would do. No commitment, and no slides.
Book a first chat
> The good news
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