DataGlue

Your guest 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, hospitality. Mia Rossi. Systems plug in: reservations, qr menu, pos, payments, loyalty, email. DataGlue joins them into one record, tagged: Fridays, table for two from Reservations; Zero-alcohol beer from QR menu; Orders the fish tacos from QR menu; Comes back monthly from Loyalty; Rebooks within weeks from Reservations; About 90 min per visit from Reservations, an estimate. The outcome, typed by you: Guests who came back: a visit, then a new booking within 30 days. 386 guests. Asked from CLAUDE: Which guests booked again after their visit? 386 guests, each with their usual night and order. Sent to email, loyalty app, bi: list updated.

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

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Sound familiar?

Your regulars know your menu by heart. Your systems still greet them as new guests.

A Monday you may recognise.

MONDAY 09:00

Reservations knows who booked. The POS knows what they ordered. Payments knows who paid. No one can say which guests came back, or how often.

MONDAY 14:00

Mia has booked a Friday table every month this year. Today she calls to book again. The host asks if she has dined with you before.

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

  1. Numbers that don't agreeReservations, the tills and payments 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 Mondays rebuilt by handExports pasted into a sheet, out of date by Friday.See the fix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 guest split into a booking, an order and a bill.See the fix
See every problem we fix and how each one is solved.

WHAT LASTS

Anyone can copy a menu or a fit-out now. Dishes, decor and booking pages are easy to match. A great night alone no longer brings guests back.

About half of public companies disappear within ten years.

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

The venues that last keep their regulars. They remember the usual table and the usual order. They invite guests back at the right moment. They make every return feel personal.

All three start with one clear view of each guest, across bookings, menus and payments. Good data design makes that possible. Gluing the pieces into context is the new moat.

Here is what that looks like in hospitality. A fictional regular, followed from one Friday to the next.

FOLLOW ONE GUEST
DEMOfictional

Meet Mia Rossi. Table 12, most Fridays.

Mia books a table for two most Fridays. She orders from the QR menu: zero-alcohol beer and the fish tacos. She pays with the card on her loyalty account. Within a few weeks of each visit, she books the next.

Your reservations system, QR menu, POS, payments and loyalty program each saw part of that. Here is the whole guest, in order.

MIA ROSSIDEMO · Mia Rossi is fictional.
  • CONFIRMEDFridays, table for twoSource: Reservations
  • CONFIRMEDZero-alcohol beerSource: QR menu
  • CONFIRMEDOrders the fish tacosSource: QR menu
  • CONFIRMEDComes back monthlySource: Loyalty
  • CONFIRMEDRebooks within weeksSource: Reservations
  • ESTIMATEAbout 90 min per visitSource: Reservations

Loyalty and order numbers link her visits. Visit length is a venue average, not a measurement of Mia.

A fictional example. Your privacy rules decide what connects. Anything missing stays missing.

01PLUG IN

Start with the systems you already run. Each holds part of Mia's Friday.

Your reservations system holds the booking. Your QR menu holds what she ordered. Your POS and payments hold the bill. Loyalty holds her visits across the year. Email shows which menus she opened.
SOURCES · fictionalDEMO
YOUR SYSTEMS
  • RESERVATIONS240 msFridays · table for two
  • QR MENU560 msZero-alcohol beer · fish tacos
  • POS880 msReceipt · table 12
  • PAYMENTS1200 msPaid · loyalty card
  • LOYALTY1520 msMember · monthly visits
  • EMAIL1840 msSpring menu · opened
ORCHESTRATIONSources together.
Evidence intact.
READY TO JOINMia RossiOne record. Every source shown.

Example systems, connected in your build. Timings show the order of this demo, not processing speed.

02GLUE

See one guest, not five records. Every link shows why it was made.

Mia books with her loyalty number. Her QR order carries the same number. The order number links the receipt and the payment. A shared table does not make every guest at it Mia.
IDENTITY · fictionalDEMO
  • Booking · table 12Reservations
  • QR orderQR menu
  • Receipt 5520POS
  • Member L-3317Loyalty
Mia Rossi4 RECORDS LINKED
  • Booking to member · her loyalty number on the bookingCONFIRMED
  • Order to member · the same loyalty numberCONFIRMED
  • Order to receipt · the same order numberCONFIRMED

A shared table does not identify every guest. Only recorded numbers link a visit.

03OUTCOMES

Ask which guests come back. In plain English.

Ask for guests who came back: a visit, then a new booking within 30 days. DataGlue shows how it counts that, step by step. Mia qualifies: a paid visit in week 1, a new booking in week 3. Open the count to see every guest in it.
OUTCOMES · fictionalDEMO
START WITH A SUGGESTED OUTCOME
  • Came back
  • Same dish twice
  • Reward used
OR DESCRIBE YOUR OWN

Guests who came back: a visit, then a new booking within 30 days

HOW DATAGLUE COUNTS IT

A paid visit, then a new booking within 30 days. The same guest, linked by loyalty number. Venue averages left out.

  • Paid visitPayments · week 1
  • New bookingReservations · week 3
  • Same guestLoyalty · member L-3317
THE RESULT

Mia qualifies. Her usual table and order travel with her.

04TAG

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

Fridays, table for two. Zero-alcohol beer. The fish tacos. Comes back monthly. About 90 minutes per visit is a venue average, so it stays marked as an estimate.
TAGS · fictionalDEMO
JOINED RECORDMia Rossi6 TAGS · 3 SOURCES
  • Fridays, table for twoCONFIRMEDSource: Reservations
  • Zero-alcohol beerCONFIRMEDSource: QR menu
  • Orders the fish tacosCONFIRMEDSource: QR menu
  • Comes back monthlyCONFIRMEDSource: Loyalty
  • Rebooks within weeksCONFIRMEDSource: Reservations
  • About 90 min per visitESTIMATESource: Reservations
CHECK THE EVIDENCE

Facts show their source. Estimates are marked and kept apart.

05SEND

Make every return personal. In the tools you already use.

Your reservations team sees her usual table and order when she books. Your loyalty app knows she is a regular. Your email tool sends the new menu to guests who opted in. BI and your warehouse keep the same records.
SEND · fictionalDEMO
CLEAN RECORD

Mia Rossi · Friday bookings, a usual order and a quick return. Example fields for the tools in your build.

ORCHESTRATIONOne record.
Your destinations.
CONNECTED IN YOUR BUILD
  • ReservationsUsual table and orderDELIVERED · 1020 ms
  • Loyalty appRegular status and visitsDELIVERED · 1600 ms
  • Your email toolNew menu · opted-in guestsDELIVERED · 2180 ms
  • BI / warehouseBookings, orders and paymentsDELIVERED · 2760 ms

Example deliveries. Routes and fields are agreed in your build. Timings show sequence, not delivery speed.

See Mia’s full journey

DEMO · Mia Rossi is fictional.

  1. Week 1 · FridayReservations

    Table for two at 7pm, booked with her loyalty number.

  2. Week 1 · 7:20pmQR menu

    Zero-alcohol beer and the fish tacos.

  3. Week 1 · 9:05pmPayments

    Bill paid. The order number links receipt and payment.

  4. Week 3Reservations

    Booked the next Friday, 13 days after her visit.

  5. Week 4 · 7:15pmQR menu

    The same order, again.

  6. Week 4 · 9:10pmPayments

    Bill paid on her loyalty card.

  7. Week 6Email

    Opened the spring menu email.

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 host knows her before she sits down.
DEMO · Mia Rossi is fictional.
“Welcome back, Mia. Table 12, and a zero-alcohol beer to start?”
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 HOSPITALITY
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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