DataGlue

Your store data is in pieces. DataGlue glues it together.

Every system you run holds part of the picture. DataGlue joins them into one view of each customer. See which customers come back, and act on it in the tools you already use.

Book a CallSee the Outcomes

Bring one decision. Get a straight answer on fit.

DEMOGLUE CONSOLEFIG. 01
SALESE-COMMERCERETAILHOSPITALITYLOGISTICSDEFENCE

Demo, retail. Sarah Chen. Systems plug in: car park camera, footfall sensor, pos, loyalty, website, email. DataGlue joins them into one record, tagged: Visits most Saturdays from Car park camera; Usually buys a flat white from POS; Loyalty member since 2023 from Loyalty; Browses sofas at night from Website; About 40 min per visit from Footfall sensor, an estimate; Opened the spring catalogue from Email. The outcome, suggested by DataGlue: Members who shop in store, then browse online within 7 days. 1240 members. Asked from GLUE CONSOLE: Which regulars shop with us in store and online? 1,240 members. Each tag shows the system it came from. Sent to loyalty app, email, ad platforms: tags updated.

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

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Sound familiar?

Your regulars come in every week. To your systems, they are still strangers.

A Monday you may recognise.

MONDAY 09:00

Footfall says Saturday was busy. The tills say sales were flat. Loyalty says members stayed home. The meeting ends on a guess.

MONDAY 14:00

Sarah walks in, as she does most Saturdays. She buys a flat white on her loyalty card and browses sofas online at night. To the store she is a stranger. The designer starts from scratch.

Your team fills the gaps by hand. Your customers feel them.

  1. Numbers that don't agreeFootfall, the tills and loyalty each count their own way.See the fix
  2. Mondays rebuilt by handExports pasted into a sheet, out of date by Friday.See the fix
  3. AI with half the factsIt reads one system at a time, then answers with full confidence.See the fix
  4. Regulars who look like strangersOne regular split into a visit, a receipt and a login.See the fix
See every problem we fix and how each one is solved.

WHAT LASTS

Anyone can match your range and your prices now. Shelves, catalogues and promotions are easy to copy. A great store alone no longer keeps your regulars.

About half of public companies disappear within ten years.

Markets, technology and customers now move faster than many companies can adapt.

The retailers that last keep their regulars, in store and online. They know a regular the moment they walk in. They follow the same customer from the shelf to the screen. They give regulars a reason to come back.

All three start with one clear view of each regular, across tills, loyalty and your website. Good data design makes that possible. Gluing the pieces into context is the new moat.

Here is what that looks like in retail. A fictional regular, followed from the car park to the sofa she buys.

FOLLOW ONE CUSTOMER
DEMOfictional

Meet Sarah Chen. Saturdays in store. Evenings online.

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

Your car park, tills, loyalty program and website each saw part of her week. Here is the whole customer, in order, through to the sofa she buys.

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 her loyalty account for parking benefits. Visit length is a mall average, not a measurement of Sarah. An email open does not prove she read it.

A fictional example. Camera and plate data connect only under your privacy rules. Estimates stay marked as estimates.

01PLUG IN

Start with the systems you already run. Each holds part of Sarah's week.

The car park camera sees plate XYZ 123 on Saturday mornings. Your tills record her flat white and loyalty card. Your website records her evening sofa browsing. Footfall, email, bookings and your CRM fill in the rest.
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 msSofa ordered
ORCHESTRATIONSources together.
Evidence intact.
READY TO JOINSarah ChenHer week. Every source shown.

Eight example systems, under your privacy rules. Visit length is an estimate. Timings show the order of this demo, not processing speed.

02GLUE

See one customer, not four records. Every link shows why it was made.

Sarah registered her plate for loyalty parking. Her loyalty account carries the email she signs in with online. That sign-in links her browsing to the same person. 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 · the email on her loyalty accountCONFIRMED
  • Email to web visitor · she signed in with that emailCONFIRMED

A similar name alone stays unlinked. Footfall stays an anonymous mall average.

03OUTCOMES

Ask what brings regulars back in. In plain English.

Ask for members who browse sofas online, then book a design consult within 30 days. DataGlue shows how it counts that, step by step. Sarah qualifies: sofa browsing on Day 8, a consult booked on Day 10. The estimated visit length is left out.
OUTCOMES · fictionalDEMO
START WITH A SUGGESTED OUTCOME
  • Repeat visitor
  • Consult booked
  • Sofa ordered
OR DESCRIBE YOUR OWN

Members who browse sofas online, then book a design consult within 30 days.

HOW DATAGLUE COUNTS IT

A sofa page visit, then a confirmed design consult within 30 days. The same person, linked through her loyalty email.

  • Browses sofas onlineWebsite · Day 8
  • Consult bookedBooking system · Day 10
  • Same personLoyalty email · confirmed link
THE RESULT

Sarah qualifies. The footfall estimate is left out of this count.

04TAG

Know every regular at a glance. Each tag shows its source.

Visits weekly. Usually buys a drink. Browses sofas online. Booked a consult. Bought the sofa. About 40 minutes per visit is a mall average, so it stays marked as an estimate.
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
  • Bought the sofaCONFIRMEDSource: CRM · Day 23
CHECK THE EVIDENCE

Facts show their source. The footfall estimate stays marked and apart.

05SEND

Make her next visit personal. In the tools you already use.

Your CRM shows the designer her visits and the sofas she viewed. Your email tool sends only what she has opted in to. Your booking system carries the consult context. BI sees the same records. Every send is logged.
SEND · fictionalDEMO
CLEAN RECORD

Sarah Chen · Saturday visits, sofa browsing, a consult and an order. Example fields for the tools in your build.

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

Example deliveries. Routes and fields are agreed in your build. Timings show 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 on her loyalty card.

  4. Day 1 · estimateFootfall sensor

    About 40 minutes per visit. A mall average, not a measurement of Sarah.

  5. Day 8 · 10:06Car park camera

    Back the next Saturday. Weekly visits show in the parking records.

  6. Day 8 · 10:15Your POS

    Another flat white on the same loyalty card.

  7. Day 8 · 21:14Website

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

  8. Day 9 · 08:40Email

    Opened the spring catalogue. An open does not prove she read it.

  9. Day 9 · 21:22Website

    Back 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 held. Room size and sofa preferences recorded.

  12. Day 21 · 16:20CRM

    Quote sent for her chosen sofa.

  13. Day 23 · 10:05CRM

    Sofa ordered.

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. Only exact matches, like the same email, join on their own.

  • 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 FOR RETAIL
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.

Start with one decision. The next, in retention, content or planning, builds on the same model.

Six more build on the same model, from daily operations to AI that sees the whole picture.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 on your team rebuilds this story by hand. Your AI reads the same gaps, only faster. Bring the question you keep 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