AI-ready data
Inventory, quality and access rights, then an internal knowledge base you can query. It is the bridge between the audit and the proof of concept: a proof of concept stumbles on the data more often than on the model.
Inventory · quality · access rights · knowledge base
- Format
- Step-by-step work on your data sources
- Who is involved
- Your IT team and the data owners in each business line
- What you leave with
- An inventory, a quality assessment, access rights in place and a knowledge base to query
- What comes next
- The proof of concept, then production
Who needs AI-ready data
This offering serves organizations with plenty of documents and data that are not yet ready to be queried by an AI tool without producing doubtful answers.
- Your documents are scattered across several tools, and nobody can say which ones are authoritative.
- Several versions of the same document circulate, and they do not say the same thing.
- Access rights are poorly defined, and you fear an AI tool could expose documents to the wrong person.
- A proof of concept stumbled on the data, and you want to know what to fix before trying again.
From inventory to knowledge base, in four steps
- Inventory: which data exists, where it lives and who answers for it.
- Quality: duplicates, gaps, formats and outdated content: what is reliable and what is not.
- Access rights: who can read what, and how those rights follow the data when AI accesses it.
- Knowledge base: the selected sources are indexed so people can query them in plain language.
How data is prepared for AI
We start from the use case you want to test, not from all of your data. Preparing everything the organization owns would take longer than the immediate need justifies, so we target the sources that feed that case, take stock of them and name an owner for each. Quality is checked on that perimeter: duplicates, gaps, formats, outdated content. An AI answer is only as good as the documents behind it.
Access rights are settled before AI touches the data, because a tool that answers everyone from the same sources exposes what some people are not allowed to read. The knowledge base therefore carries over the rights of each source: a person gets answers drawn only from documents they can already open. Answers point back to the source documents, so a statement can be checked at the source.
What you get
Inventory and quality review
The sources your use case needs, their content and their owner, then a finding on duplicates, gaps and outdated content that separates the reliable from the doubtful.
Access rights set in advance
Who can read what is defined before an AI tool reaches the sources, and those rights follow the data when the tool answers, so nobody sees what is closed to them.
A queryable knowledge base
The selected sources are indexed so people can query them in plain language, with answers that point back to the source documents and respect each person's rights.
After preparing your data for AI
Once the sources are ready, a proof of concept tells you more: if it fails, the cause no longer hides in the data. When the knowledge base gives good answers on the chosen case, production moves it to daily use, with monitoring of answer quality, costs and incidents.
Frequently asked questions
Which data should we prepare first?
The data for the use case you want to test. We take stock of the sources involved, check their quality and access rights, then build the knowledge base on that perimeter.
What is an internal knowledge base you can query?
Your documents and reference data are indexed so people can query them in plain language. Answers point back to the source documents and respect each person's access rights.
Does our data have to leave our organization?
That depends on your needs. Models can be installed on your premises or hosted in Québec, and governance settles the question before the data is prepared.
What happens with contradictory versions of the same document?
The inventory gives each source an owner, and the quality step flags duplicates and outdated content. The owner designates the reference version; the knowledge base indexes only the sources retained, so discarded versions do not appear in it.