DataGlue

Your data is in pieces. DataGlue glues it together.

Plug in the systems you already run. DataGlue joins the pieces into one record, suggests outcomes or takes yours, tags every record, and sends clean data wherever you work.

Book a CallSee the Outcomes

Bring one decision. Get a straight answer on fit.

DEMOGLUE CONSOLE · SOURCES TO OUTCOMESFIG. 01

Demo, sales. Systems plug in: ad clicks, website, booking form, booking system, crm, email. DataGlue joins them into one record for Alex Morgan, tagged: Came from a search ad from Ad click; Read pricing twice from Website; Left the form at step 2 from Booking form; Booked a call from Booking system; Proposal sent from CRM; Deal signed, Day 23 from CRM. The outcome, typed by you: People who booked a discovery call, then signed within 30 days. 12 people. Asked from Claude via MCP: Which booked calls became signed deals this month? 12 people. Each one shows the ad, the pages and the call that led there. Sent to your crm, ad platforms, email: deal signed.

DEMO, FICTIONAL PEOPLE. YOUR SYSTEMS, YOUR PRIVACY RULES.

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Here's an example of how DataGlue brings your many systems together. One fictional customer. One connected story.

FOLLOW ONE CUSTOMER
DEMOfictional

Meet Sarah Chen. The mall. Her home. One story.

Sarah is a regular at a mall furniture store. Most Saturdays, she parks on level 2, stops for a flat white and looks around. At home, she browses sofas after 9pm.

Each system holds a piece of her week. See how DataGlue joins the visits, purchases and online browsing, then adds her design consult and the deal she signs.

WHO IS SARAH?DEMO · Sarah Chen is fictional.
  • CONFIRMEDParks on level 2 most SaturdaysSource: Car park camera · plate XYZ 123
  • ESTIMATEAbout 40 minutes per visitSource: Footfall sensor · mall average
  • CONFIRMEDFlat white at the caféSource: Your POS · loyalty card used
  • CONFIRMEDLoyalty member since 2023Source: Loyalty · member L-2048
  • CONFIRMEDBrowses sofas online after 9pmSource: Website · signed-in visits
  • CONFIRMEDOpened the spring catalogue emailSource: Email · recorded open

Sarah added her plate to loyalty and opted in to parking benefits. The visit duration is a mall average, not a measured visit for Sarah. A recorded email open does not prove she read it.

Use camera and plate data only under your privacy rules. Estimates are marked and can be switched off.

01PLUG IN

Bring her whole week together. Plug in the systems you use.

The car park camera records XYZ 123. Your POS records her flat white and loyalty card. Your website records her evening sofa visits. Connect these with footfall, loyalty, email, your booking system and CRM. Each piece keeps its source and time.
SOURCES · fictionalDEMO
YOUR SYSTEMS
  • Car park camera240 msXYZ 123 · Saturdays
  • Footfall sensor560 msMall average · ~40 min
  • Your POS880 msFlat white · L-2048
  • Loyalty1200 msMember since 2023
  • Website1520 msSofas · after 9pm
  • Email1840 msSpring catalogue open
  • Booking system2160 msDesign consult booked
  • CRM2480 msDeal signed
ORCHESTRATIONSources together.
Evidence intact.
READY TO JOINSarah ChenHer week. Sources attached.

Eight sources, under your privacy rules. Footfall duration is an estimate. Timings show this demo's sequence, not processing speed.

02GLUE

Glue the pieces into one Sarah. Keep the evidence behind each link.

Sarah registered plate XYZ 123 for loyalty parking benefits. Her loyalty card carries the same email she uses online. A signed-in visit links the web visitor to that email. The console shows those exact links. A similar name alone stays unlinked.
IDENTITY · fictionalDEMO
  • Plate XYZ 123Car park camera
  • Card L-2048Loyalty
  • Email (masked)Loyalty + website
  • Visitor 7f3cWebsite
Sarah Chen4 RECORDS LINKED
  • Plate to loyalty card · Sarah registered it and opted inCONFIRMED
  • Loyalty card to email · email on her member recordCONFIRMED
  • Email to web visitor · same signed-in emailCONFIRMED

A similar name alone stays unlinked. Anonymous footfall data remains a mall average.

03OUTCOMES

See what brings visitors back. Choose the outcome in plain English.

Ask for weekly mall visitors who browse online, then book a design consult. DataGlue shows the rule and its sources. Sarah's Saturday parking visits, evening sofa browsing and confirmed consult qualify. The estimated visit duration is left out of the count.
OUTCOMES · fictionalDEMO
START WITH A SUGGESTED OUTCOME
  • Weekly visitor
  • Consult booked
  • Deal signed
OR DESCRIBE YOUR OWN

Weekly mall visitors who browse online, then book a design consult.

HOW DATAGLUE COUNTS IT

A parking visit in each of the last two weeks, then a sofa page visit and a confirmed design consult. All linked to the same person within 30 days.

  • Visits weeklyCar park camera · Day 1 and Day 8
  • Browses sofas onlineWebsite · Day 8 and Day 9
  • Consult bookedBooking system · Day 10
THE RESULT

Sarah matches. The footfall estimate is excluded from this count.

04TAG

Give her habits useful tags. Facts and estimates stay distinct.

Sarah's tags span both worlds: visits weekly, usually buys a drink, browses sofas online, consult booked and deal signed. Every tag points to its source. About 40 minutes per visit stays an estimate from mall footfall data. You can switch estimates off.
TAGS · fictionalDEMO
JOINED RECORDSarah Chen6 TAGS · 6 SOURCES
  • Visits weeklyCONFIRMEDSource: Car park camera · Saturday records
  • Usually buys a drinkCONFIRMEDSource: Your POS · loyalty purchases
  • Browses sofas onlineCONFIRMEDSource: Website · signed-in visits
  • About 40 minutes per visitESTIMATESource: Footfall sensor · mall average
  • Consult bookedCONFIRMEDSource: Booking system · Day 10
  • Deal signedCONFIRMEDSource: CRM · Day 23
CHECK THE EVIDENCE

Confirmed tags come from recorded events. The footfall estimate stays separate and can be switched off.

05SEND

Put her context to work. Send it to the tools you connect.

Your CRM gets Sarah's store visits and sofa interest before the consult. Your email tool gets the catalogue interest she has opted in to receive. Your booking system gets the consult context. BI gets the same sourced outcomes. Choose the fields each destination needs. Every send is logged.
SEND · fictionalDEMO
CLEAN RECORD

Sarah Chen · mall visits, sofa interest, consult and deal. Choose the fields each destination needs.

ORCHESTRATIONOne record.
Your destinations.
CONNECTED IN YOUR BUILD
  • Your CRMMall visits, sofa interest and consultDELIVERED · 1020 ms
  • Your email toolCatalogue interest · opted-in membersDELIVERED · 1600 ms
  • Your booking systemDesign consult and customer contextDELIVERED · 2180 ms
  • BI / warehouseClean events, tags and sourcesDELIVERED · 2760 ms

Every send in one log. Retry a single row. Timings illustrate the demo sequence, not delivery speed.

See Sarah’s full journey

DEMO · Sarah Chen is fictional.

  1. Since 2023Loyalty

    Joined as member L-2048. Added plate XYZ 123 and opted in to parking benefits.

  2. Day 1 · 10:02Car park camera

    Plate XYZ 123 recorded on level 2 under the mall's privacy rules.

  3. Day 1 · 10:11Your POS

    Bought a flat white at the café. Used her loyalty card.

  4. Day 1 · estimateFootfall sensor

    About 40 minutes per visit. A mall average used as an estimate, not a measured visit for Sarah.

  5. Day 8 · 10:06Car park camera

    Returned the next Saturday. Weekly visits appear in the parking records.

  6. Day 8 · 10:15Your POS

    Another flat white, recorded against the same loyalty card.

  7. Day 8 · 21:14Website

    Browsed sofas at home while signed in with her loyalty email.

  8. Day 9 · 08:40Email

    Spring catalogue open recorded. An open does not prove she read it.

  9. Day 9 · 21:22Website

    Returned to the sofa collection after 9pm.

  10. Day 10 · 09:03Booking system

    Booked a design consult with the furniture store.

  11. Day 13 · 11:30CRM

    Design consult completed. Room measurements and sofa preferences recorded.

  12. Day 21 · 16:20CRM

    Proposal sent for her chosen sofa.

  13. Day 23 · 10:05CRM

    Deal signed.

Check any number before you act on it.

  • Source on every fact. Each step keeps its system and its time.

  • Matched on evidence. Links are confirmed, candidate or conflict. Nothing merges silently.

  • Rows behind every count. Open any number to see the people in it.

The design consult starts with context.DEMO · Sarah Chen is fictional.
“You were looking at sofas online. Let's talk about your room.”
Want this view of your own customers? Bring one decision. We'll trace its systems with you.Book a Call

WHAT IT CAN DO

What DataGlue can do for you.

Pick your sector. Each question joins systems you already run.

QUESTIONS BY SECTOR
156systems and counting.If it can send an event, DataGlue can take it. Webhooks and our API take events from any system. Postgres plugs in directly; other databases and warehouses arrive through a sync you run.All integrations

Example questions. We agree the sources and rules in your build.

Explore all outcomes
WHAT WE GLUE TODAY

Keep your tools. Connect your systems: CRM, cameras, POS, IoT sensors, QR menus, ticketing, loyalty cards and warehouse.

No migration. Our team connects what you already run. AI can reach into each silo; DataGlue joins them into one model, and each fact keeps its source.

  1. Before they get in touch

    Visits, ad clicks and form behaviour, kept by our website script.

  2. While you get to know them

    Bookings, call results, emails and CRM updates.

  3. When they become a customer

    Deals, payments, POS sales, loyalty cards and event tickets, as your systems record them.

  4. Out in the real world

    Cameras, number plate readers, IoT sensors, QR menus, Wi-Fi and GPS trackers.

Browse all 156 systems and the route each one takes.

We agree the connections in your build. Offline activity needs a record from your systems.

Describe the outcome you want. DataGlue works it out and shows the formula in plain English.

It reads the definitions of your data sources. Outcomes feed any LLM, BI tool, report or destination you choose.

OUTCOMES

Start with a question that matters.

One decision first. The next ones, in content, analysis or workforce planning, reuse the same model.

Six more run on the same model, from operations to AI with the missing context.See the Outcomes
WHO BUILDS IT

Built with you.

A dedicated team does the connecting. Your team does the deciding.

  1. Bring one decision.

    We trace where its data lives and give you a straight answer on fit.

  2. Agree the build.

    We scope the first outcome and price it before work begins. No seat licences.

  3. Put it to work.

    Our team connects your systems, with no migration. You keep the context and the console.

FROM THE FOUNDER
“We don't do sales calls. We do deep architectural reviews.”
Ankit PaliwalFounder, DataGlue

One answer.And the path behind it.

Every Monday, someone rebuilds this story by hand. Your AI now reads the same gaps, faster. Start with the question your team keeps coming back to.

  • Priced to the outcome. Scoped and agreed before work begins. No seat licences.

  • Yours to keep. Outcomes feed any LLM, BI tool or destination. Sessions, events and identity links export on request.

  • A straight answer on fit. If we're not the right team for it, we'll say so on the call.

BRING ONE DECISIONBook a CallWHAT TEAMS BUILD FIRSTSee the Outcomes

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  • Ticketek
  • me&u
  • InvestorKit
  • Team Global Express