DataGlue vs asking AI with exported spreadsheets
AI can help with a spreadsheet. Give it the wider story.
Compare asking about an export with giving your chosen LLM linked business facts and visible outcome rules.
Talk it through with the team that would build it.
Start with the right fit.
When the alternative fits
An exported spreadsheet can be enough for a small, one-off question when the data is prepared, current and appropriate to share. AI can help explore the rows, explain a formula or suggest questions to investigate.
What DataGlue brings
DataGlue joins the recorded sources behind the question and defines the outcome as a visible formula. Tags and source-backed context can reach your chosen LLM through an agreed route. Better context helps ground the question; it does not guarantee a correct answer.
Plan for the work around it.
An export only contains the fields and dates you included. It may miss support events, later payments or the keys that link a customer across systems. The model cannot check facts it was never given, and its answer still needs review.
Compare the job each does.
| The job | asking AI with exported spreadsheets | DataGlue |
|---|---|---|
| Starting point | asking AI with exported spreadsheetsA file selected for the current question. | DataGlueThe sources and event rule behind a named result. |
| Context | asking AI with exported spreadsheetsThe fields, dates and explanations included in the prompt. | DataGlueLinked records and the meaning of the outcome tag. |
| Identity | asking AI with exported spreadsheetsMatches need to be prepared or explained in the export. | DataGlueSupported identity links with uncertainty visible. |
| Updates | asking AI with exported spreadsheetsExport again or maintain your own refresh process. | DataGlueSource refresh and delivery timing agreed in the build. |
| Model choice | asking AI with exported spreadsheetsYour chosen model and file workflow. | DataGlueBring your own LLM through an agreed context route. |
| Checking answers | asking AI with exported spreadsheetsReview against the file and underlying sources. | DataGlueReview against the supplied records and visible rule. |
Make the next steps clear.
01
Ask one real question
Choose the decision before sending more rows to a model.
02
Check what the file leaves out
Find the other systems, dates and identity links needed for a useful answer.
03
Keep the evidence close
Give the model the joined context and check its answer against the source records.
The steps that earn a tag.
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.
Build on the work you trust.
Keep using spreadsheets and AI for exploration. When a question repeats or spans changing systems, DataGlue can supply joined context to the LLM you already use. The export can still be useful for spot checks and analysis.
Good questions. Clear answers.
Are spreadsheets ever enough for AI?
Yes. A small, well-prepared file may answer a one-off question. Check that its fields and dates cover what you are asking and verify the answer against the source.
Does DataGlue replace our AI model?
No. Bring your own LLM. DataGlue builds the joined business context and agrees a route for supplying it to your chosen destination.
Will better context prevent hallucinations?
No. It gives the model more useful evidence, but the model can still misread it or produce an unsupported answer. Check the result against the records.
Find your fit
Explore another comparison
- Approach
DataGlue vs building it in-house
Your engineers. Which work should they own?
Compare owning every data task with building one joined outcome alongside a dedicated team.
- Alternative focus: You coordinate the build
- DataGlue focus: Build with a dedicated team
See the comparison
- Approach
DataGlue vs a one-off data consultancy project
A finished project. Who carries the rule forward?
Compare a defined consultancy project with an outcome build whose rules and destinations are agreed with your team.
- Alternative focus: A defined project handover
- DataGlue focus: An outcome built with you
See the comparison
Build it with us
Bring one result you want to find.
A dedicated team maps the sources, checks the fit and builds it with you. Scope and price are agreed with the team.