A connector sees one tool
The assistant reads CRM data while support holds the latest problem.
DataGlue for AI & product teams
My AI needs the customer story, not another data silo.
Product events show one part of an account. Sales, support and billing hold the rest. DataGlue joins the recorded context so your team can inspect what an answer uses.
A dedicated team builds it with you. Start with one decision.
Product eventsWebsiteCRMSupport
DataGlue
Show the formula.
Which events. In what order.
Example tagSet-up trial became a paid account
To your CRM, ads, BI, email or chosen LLM.
Connections and destinations agreed in the build.
Sound familiar?
You could connect the assistant. You just can’t give it one story.
Three IDs look like three customers
Product, sales and billing use separate account keys.
A confident answer hides its inputs
Your team cannot check a conclusion without seeing what it read.
Context gets rebuilt for each model
Changing an AI tool means mapping the same sources again.
A joined picture
Join the context before you ask.
Start with a result that crosses product and business systems. DataGlue shows the recorded steps and their match evidence. Your chosen LLM can receive that context through an agreed build route.
Systems usually joined
- Product events
- Website
- CRM
- Support
- Billing
- Warehouse
Example 01
- Product: trial setup completed
- CRM: demo attended
- Billing: first subscription paid
In this order, within 14 days, on the same confirmed account.
Events → tagSet-up trial became a paid account
Example 02
- Support: issue opened
- Support: issue resolved
- Product: customer returned to the feature
In this order, within 7 days, linked to the same confirmed customer and feature.
Events → tagReturned after support resolution
Example 03
- CRM: renewal review completed
- Billing: renewed subscription paid
In this order, within 30 days, on the same confirmed account.
Events → tagReviewed, renewed and paid
Example rules, not customer results. We agree source access, match keys, time windows and tag destinations in your build.
How we work
One question. Five steps with your team.
The same build process, shaped around your decision and its sources.
Book a call
Bring one question.
Talk it through with the people who would build it. We find where its data lives today.
Working session
A working session to map your data.
We map every source behind the decision, then show you what it looks like glued together. You get the scope and plan before work begins.
Build together
We connect the pieces.
A dedicated team finds the signals, glues them into one living model and matches records on evidence. Your systems stay where they are.
Go live
Your first result, ready to use.
We take the first result live in your Glue Console. Ask in plain words, see what each step read, and open the counts to their rows.
Keep building
Your team keeps the Console.
Then the next outcome builds on the same model. The rollout runs in phases, each measured against the outcome.
What you get
Useful context. In the tools you use.
Context across teams
Join product use with sales, support and billing.
Sources to inspect
Check the records behind the context.
Visible account matches
Separate confirmed links from candidates.
A rule for the result
Show the events and order that earn a tag.
Your chosen LLM
Connect your AI tools as part of the build scope.
Portable context
Reuse the joined model when destinations change.
See the working
Check the approach before the call.
Read the published outcome examples, source details and comparisons behind this approach. They explain the method; they are not measured results for your role.
Trusted by
Fair questions
What you might be wondering.
Couldn’t I just connect our systems to an AI assistant?
Yes, for access to their records. You still need to decide which IDs belong to one account and what each result means. DataGlue does that joining with your team, then makes the sourced context available to the tools you choose.
Can we bring our own LLM?
Yes. Wiring context into your chosen LLM is agreed as part of an Outcome Build. Ask AI in the Glue Console uses a model we set up; connecting your own AI tools is a separate build route.
Do we need a data team or clean data first?
No. Bring one question and the names of the systems behind it. A dedicated team maps the sources, joins the records and builds the result with you. Your team sets ownership, privacy and quality rules.
What does it cost, and what happens on the call?
The first call checks the outcome, the sources and the fit. No system access is needed for that conversation. If it fits, we map the data in a working session. The build is a separate engagement, scoped and priced before work begins; there is no public rate card.
Your next step
Bring the question you keep coming back to.
We’ll talk through the outcome, where its data lives and whether a build fits. No system access needed for the first call.
Book a Call
Free first call. Scope and price agreed before a build begins.
Is it a fit?
An honest fit check.
A good fit
This may suit you if…
- Your AI or product question needs business data beyond the app.
- You want match evidence and source records to inspect.
- You can name the destination and its permitted data use.
Probably not a fit
This may not suit you if…
- You only need feature analytics inside one app.
- You want AI to fill gaps in records with invented facts.
- You need an instant connection to every model without setup.