Logistics
- Which customers had two or more late deliveries in 14 days?
- Which late deliveries also had a temperature breach?
- Which customers had a late delivery, then complained?
- Fleet
- Sensors
- CRM
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.
Bring one decision. Get a straight answer on fit.
Demo, logistics. Route 7 · Truck 482. Systems plug in: gps tracker, temp sensor, gate scanner, delivery app, traffic feed, crm. DataGlue joins them into one record, tagged: Late after 4pm from GPS tracker; 38 min at Depot B from Gate scanner; Cold-chain breach from Temp sensor; Customer complaint from CRM; Contract review in May from CRM; Heavy traffic from Traffic feed, an estimate. The outcome, suggested by DataGlue: Customers with a late delivery, then a complaint within 7 days. 14 accounts. Asked from CHATGPT: Which customers should hear from us before they call? 14 accounts, each with the late run and the complaint attached. Sent to dispatch, your crm, bi: accounts flagged.
DEMO, FICTIONAL PEOPLE. YOUR SYSTEMS, YOUR PRIVACY RULES.
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Sound familiar?
A Monday you may recognise.
MONDAY 09:00
GPS shows a late arrival. The gate log shows a 38-minute wait at the depot. The temperature sensor shows a breach. No one can see that all three were the same delivery.
MONDAY 14:00
Your largest grocery customer calls to review their contract. Route 7 ran late twice this month. Your account manager hears it from them first.
Your team fills the gaps by hand. Your customers feel them.
WHAT LASTS
Markets, technology and customers now move faster than many companies can adapt.
The carriers that last keep customers after a late delivery. They see the problem before the customer calls. They explain what happened, with the records to show it. They give customers a reason to stay.
All three start with one clear view of each account, across every run, scan and complaint. Good data design makes that possible. Gluing the pieces into context is the new moat.
Truck 482 carries chilled freight on Route 7, from Depot B to a grocery customer in the city. This month it waited at the depot, arrived after 4pm and logged a temperature breach. The customer noticed.
Your GPS, sensors, gate scanner, delivery app and CRM each saw part of that. Here is the whole run, in order, beside the account it affects.
Run, truck and delivery numbers link the records. Traffic is an estimate from a feed. It does not prove why a delivery ran late.
A fictional example. Your privacy rules decide what connects. Anything missing stays missing.
Sources together.Example systems, connected in your build. Timings show the order of this demo, not processing speed.
Similar times alone never link a run. A traffic estimate is not a recorded cause.
Customers with a late delivery, then a complaint within 7 days
A late delivery, then a complaint from the same customer within 7 days. Scheduled and arrival times from your systems. Traffic estimates left out.
The grocery account qualifies. Both late runs are attached.
Facts show their source. Estimates are marked and kept apart.
Route 7 · Truck 482 · two late runs, a breach and a complaint. Example fields for the tools in your build.
One record.Example deliveries. Routes and fields are agreed in your build. Timings show sequence, not delivery speed.
DEMO · Route 7 · Truck 482 is fictional.
Truck 482 waited 38 minutes at Depot B.
Run assigned to Route 7. Due at 2pm.
Cold-chain breach recorded on the run.
Arrived after 4pm.
Late again on Route 7.
Complaint logged with the delivery reference.
Heavy traffic. An estimate, not a cause.
Contract review set for May.
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.
“Route 7 ran late twice this month. Here is what happened, and what we have changed.”
WHAT IT CAN DO
Pick your sector. Each question joins systems you already run.
Example questions. We agree the sources and rules in your build.
Explore all outcomesNo 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.
Visits, ad clicks and form behaviour, kept by our website script.
Bookings, call results, emails and CRM updates.
Deals, payments, POS sales, loyalty cards and event tickets, as your systems record them.
Cameras, number plate readers, IoT sensors, QR menus, Wi-Fi and GPS trackers.
We agree the connections in your build. Offline activity needs a record from your systems.
It reads the definitions of your data sources. Outcomes feed any LLM, BI tool, report or destination you choose.
Start with one decision. The next, in retention, content or planning, builds on the same model.
A dedicated team does the connecting. Your team does the deciding.
We trace where its data lives and give you a straight answer on fit.
We scope the first outcome and price it before work begins. No seat licences.
Our team connects your systems, with no migration. You keep the context and the console.
“We don't do sales calls. We do deep architectural reviews.”
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.
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