DataGlue

Your order 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, e-commerce. Priya Shah. Systems plug in: ad clicks, storefront, orders, returns, support desk, email. DataGlue joins them into one record, tagged: Came from a social ad from Ad click; Left a cart, came back from Storefront; Returned the grinder from Returns; Reorders every five weeks from Orders; Opens refill reminders from Email; Asked about grind size from Support desk. The outcome, suggested by DataGlue: Repeat customers: two or more paid orders in the last 45 days. 418 customers. Asked from CHATGPT: Which customers keep coming back? 418 customers. Each one shows every order, any return and the reorder. Sent to email, ad platforms, bi: repeat buyer.

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

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Sound familiar?

Every order tells you what sold. None of them tells you who comes back.

A Monday you may recognise.

MONDAY 09:00

The ad report claims the sales. The store counts the orders. Finance counts the refunds. No one can say which new customers ever came back.

MONDAY 14:00

Your best customer has ordered every five weeks for a year. Today they order again. Your email tool still sends them a first-order discount.

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

  1. Numbers that don't agreeThe ad report, the store and finance 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 Mondays rebuilt by handExports pasted into a sheet, out of date by Friday.See the fix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. Customers who look like strangersOne customer split into a session, an order and a return.See the fix
See every problem we fix and how each one is solved.

WHAT LASTS

Anyone can launch a store from a prompt now. Products, pages and prices are easy to copy. A great product alone no longer brings customers back.

About half of public companies disappear within ten years.

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

The stores that last keep their repeat customers. They remember what each customer bought. They send the reminder when it is useful. They give customers a reason to order again.

All three start with one clear view of each customer, across orders, returns and email. Good data design makes that possible. Gluing the pieces into context is the new moat.

Here is what that looks like in e-commerce. A fictional customer, followed from a first order to a habit.

FOLLOW ONE CUSTOMER
DEMOfictional

Meet Priya Shah. From one order to a habit.

Priya found your store through a social ad. She left a cart, came back from your reminder email and ordered coffee and a grinder. She returned the grinder. She kept ordering the coffee.

Your storefront, orders, returns, support desk and email each saw part of that. Here is the whole customer, in order.

PRIYA SHAHDEMO · Priya Shah is fictional.
  • CONFIRMEDCame from a social adSource: Ad click
  • CONFIRMEDLeft a cart, came backSource: Storefront
  • CONFIRMEDReturned the grinderSource: Returns
  • CONFIRMEDReorders every five weeksSource: Orders
  • CONFIRMEDOpens refill remindersSource: Email
  • CONFIRMEDAsked about grind sizeSource: Support desk

The social ad is read from the landing page address. Orders, refunds and tickets come from your own systems.

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 Priya.

Your storefront saw the social ad, the product pages and the cart she left. Your order system holds three orders. Returns holds the grinder. Your support desk holds her question about grind size. Your email tool saw which reminders she opened.
SOURCES · fictionalDEMO
YOUR SYSTEMS
  • AD CLICKS240 msSocial ad · landing page
  • STOREFRONT560 msCart left · Day 1
  • ORDERS880 ms3 orders · every five weeks
  • RETURNS1200 msGrinder · refunded Day 12
  • SUPPORT DESK1520 msGrind size question · Day 9
  • EMAIL1840 msRefill reminder · opened
ORCHESTRATIONSources together.
Evidence intact.
READY TO JOINPriya ShahOne record. Every source shown.

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

02GLUE

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

Priya checked out with the email on her customer account. Checkout carries the browsing that came before it. The return and the support ticket carry the same order number. A similar name on another order stays unlinked.
IDENTITY · fictionalDEMO
  • Visitor 4b1eStorefront
  • Order #1042Orders
  • Priya ShahCustomer account
  • Ticket 3381Support desk
Priya Shah4 RECORDS LINKED
  • Visit to order · checkout carries the visitor IDCONFIRMED
  • Order to account · the same customer emailCONFIRMED
  • Ticket to order · the same order numberCONFIRMED

Priya confirmed her email at checkout. A similar name on another order stays unlinked.

03OUTCOMES

Ask who comes back. In plain English.

Ask for repeat customers: two or more paid orders in the last 45 days. DataGlue shows how it counts that, step by step. Priya qualifies with orders on Day 3 and Day 34. The return did not end the relationship.
OUTCOMES · fictionalDEMO
START WITH A SUGGESTED OUTCOME
  • Repeat customer
  • Subscribed
  • Refund issued
OR DESCRIBE YOUR OWN

Repeat customers: two or more paid orders in the last 45 days

HOW DATAGLUE COUNTS IT

Paid order, at least twice in the last 45 days. The same confirmed customer. Refunds stay visible.

  • First orderOrders · Day 3
  • Second orderOrders · Day 34
  • Same customerCheckout email · confirmed
THE RESULT

Priya qualifies. The return is on her record, and so is the reorder.

04TAG

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

Came from a social ad. Left a cart, then came back. Returned the grinder. Reorders every five weeks. Opens refill reminders. Each tag is something Priya did, not a guess about why.
TAGS · fictionalDEMO
JOINED RECORDPriya Shah6 TAGS · 6 SOURCES
  • Came from a social adCONFIRMEDSource: Ad click
  • Left a cart, came backCONFIRMEDSource: Storefront
  • Returned the grinderCONFIRMEDSource: Returns
  • Reorders every five weeksCONFIRMEDSource: Orders
  • Opens refill remindersCONFIRMEDSource: Email
  • Asked about grind sizeCONFIRMEDSource: Support desk
CHECK THE EVIDENCE

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

05SEND

Make the next order effortless. In the tools you already use.

Your email tool knows she is a regular, so she gets a refill reminder, not a first-order discount. Your ad platforms can learn she is already a customer. BI sees repeat rate by first product. Your warehouse keeps the complete record.
SEND · fictionalDEMO
CLEAN RECORD

Priya Shah · social ad, three orders, one return. Example fields for the tools in your build.

ORCHESTRATIONOne record.
Your destinations.
CONNECTED IN YOUR BUILD
  • Your email toolLast order and customer statusDELIVERED · 1020 ms
  • Ad platformsExisting customers, not new onesDELIVERED · 1600 ms
  • BIRepeat rate by first productDELIVERED · 2180 ms
  • WarehouseOrders, returns and ticketsDELIVERED · 2760 ms

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

See Priya’s full journey

DEMO · Priya Shah is fictional.

  1. Day 1Ad click

    Arrived from a social ad, read from the landing page address.

  2. Day 1 · cartStorefront

    Added the house blend and a hand grinder. Left the cart.

  3. Day 2Email

    Opened the cart reminder.

  4. Day 3Orders

    First order: house blend 1kg and a hand grinder.

  5. Day 9Support desk

    Asked which grind suits her machine.

  6. Day 12Returns

    Grinder returned and refunded.

  7. Day 32Email

    Opened a refill reminder.

  8. Day 34Orders

    Second order: house blend 1kg.

  9. Day 70Orders

    Third order. Switched to a subscription.

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 reminder arrives when she needs it.
DEMO · Priya Shah is fictional.
“Running low on the house blend? Your usual 1kg bag is one click away.”
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 E-COMMERCE
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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