A common problem / a joined result
AI gives generic answers about our business
Give your chosen LLM joined facts, their sources and the rule behind the result.
Talk through the sources behind your question.
Sound familiar?
You ask about your customers or orders and get an answer that could fit any business. Copying a few spreadsheets into the prompt still leaves missing context.
The pieces live apart.
The model only sees what you give it. Sales, support and delivery records are separate, and exported rows can lose the identity links, dates and result definitions that make them useful together.
CRM
The confirmed order
Logistics
The delivery event
Ticketing
The open issue
Each source has a piece. The join gives the pieces a shared meaning.
One rule. A useful result.
Example rule / events → tag
CRM: order confirmed
Logistics: delivery recorded
Ticketing: issue opened
In this order, within 7 days, on the same order ID.
Resulting tag
Delivered order with a recorded issue
Illustrative rule. Sources, match keys and timing are agreed in the build.
What you get
- Business context built from linked source records.
- A visible rule behind each supplied outcome tag.
- An agreed route to your own LLM.
Systems usually joined: CRM, Logistics, Ticketing. The team checks the records, match keys and destination with you.
A question that crosses teams.
Leaders, support teams and operations teams using AI to ask about business results.
Good questions. Clear answers.
Do we have to use a particular AI model?
No. Bring your own LLM. DataGlue is destination-agnostic, and the team agrees how the joined context will reach your chosen model.
Will joined context stop AI making mistakes?
No. Better source context gives the model more useful facts, but its answers still need checking against the records and the question you asked.
Are spreadsheets ever enough?
Yes. A small, well-prepared export can suit a one-off question. Joined context helps when the question repeats or needs facts from several changing systems.
Follow the next piece.
Related problems
Definitions & comparisons
Build it with us
Bring the problem. We'll map the pieces.
A dedicated team checks the sources, defines the result and builds it with you. Scope and price are agreed with the team.