Every offline workspace already knows things. Work, time, people, movement. We call that memory layer StoreGraph our ontology of how a workspace runs. AI Hub reads it, and turns it into decisions you approve, actions it runs, and results that come back.
AI Hub runs on our ontology, the technology that
turns how an offline workspace runs into decisions.
Better calls and immediate action, grounded in your site's context.
No tools for deciding
[lack of decision making tool]
Better calls and immediate action, grounded in your site's context.
No tools for deciding
[lack of decision making tool]
How we solve it
From collecting datato solving problems
We capture field data that was never recorded, and pull your existing systems into one stream.
Relationships and meaning get added to the data, so AI understands how your operation actually runs.
An agent that knows your industry's context decides what needs doing now.
We design the human checkpoints together, so decisions carry through to real operations.
Collect data
Not one general AI ,
a specialist for every job.
From data collection and processing
to agents doing the work.
AI HUB target architecture, 12 layers
An extensible structure that separates customer experience, the intelligence organization, shared memory, and the execution and operations foundation.
L1
Channel · Customer Experience
- Web/App
- Chat
- Dashboard
- Report
- Notification
Every layer owns its responsibility.
Customer Experience
Composes the pages and Decision Cards that owners and HQ see. Questions, reports, approvals, and notifications converge on this layer.
Knows the domain.
Works with people.
AI Agent, built to decide.
The rush doesn't run on a schedule
In F&B, the challenge is how fast you decide at peak time.
No demand forecast
Order volume shifts daily, so prep runs on the manager's experience.
Stock blind to orders
Sales and stock live in separate systems, no real-time count.
Staffing by gut feeling
No data says when the rush hits, so scheduling runs on instinct.
Decisions that finish prep before the rush
It's designed to read orders, inventory, weather, and staffing as one context, flagging peak-time prep and stockout risk before they hit.
Ontology
ORDER
84%
relation coverage
Data schema
Data schema
Data schema
INVENTORY
consumes
STAFF
handles
WEATHER
affects
Event Stream
Stream
relations mapped in realtime
DEVICE
LOG
TIME
STATUS
CC-MHYYSSPF
[Weather → Order] rain signal, delivery weighted
14:30:12
CC-MV1-2X4J
[Payment] approved, ₩12,500
14:31:40
CC-MHYYSSPF
[Order → Inventory] bean stock deduction linked
14:31:55
CC-MV1-2X4J
[Order] Americano (HOT) order received
14:32:07
CC-MV1-2X4J
[Staff → Order] peak-time staffing matched
14:20:05
CC-MRLNBYXL
[Inventory] bean level 20% detected
14:22:18
CC-MV1-2X4J
[Order] Americano (HOT) order received
14:32:07
Order surges never come without warning
In delivery, the challenge is being ready before the surge.
No surge forecast
Spikes get confirmed after they pass, so prep always starts late.
Kitchen and dispatch out of sync
Cook-finish and rider arrival drift apart, cold food, idle riders.
Uneven demand across zones
No real-time read by zone, idle riders here, shortages there.
Dispatch that reads the surge first
It's designed to read order flow and zone signals together, forecasting surges and proposing rider placement and cook-start timing ahead of them.
Ontology
ORDER
91%
relation coverage
Data schema
Data schema
Data schema
RIDER
pre-positions
ZONE
rebalances
KITCHEN
preps
Dispatch Stream
Stream
relations mapped in realtime
DEVICE
LOG
TIME
STATUS
DV-SG04-K1
[ETA] delay risk detected, segment 3
18:38:19
DV-KT02-P7
[Order → Kitchen] optimal prep time linked, 12 min
18:40:30
DV-SG04-K1
[Rider → Zone] pre-positioning suggested, zone 4
18:41:52
DV-SG04-K1
[Surge] order spike forecast 92%, 19:00
18:42:11
DV-SG04-K1
[Zone → Rider] rebalance confirmed
18:32:44
DV-KT02-P7
[Dispatch] 3,924 decisions today
18:35:02
DV-SG04-K1
[Surge] order spike forecast 92%, 19:00
18:42:11
Stockouts don't start in the warehouse , they start in the structure
In logistics, the challenge is moving before the stockout.
Threshold breaches caught late
Low stock surfaces only at outbound, emergency orders, extra cost.
Dispatch and dock out of step
Truck arrivals and dock slots are managed apart, so waiting piles up.
Approval bottlenecks
Replenishment drafts pass hand to hand, stretching lead time.
Replenishment and dispatch that move before the gap
It's designed to read inventory, fleet, and dock together, drafting replenishment before stockouts, with the approval points designed in with you.
Ontology
STOCK
87%
relation coverage
Data schema
Data schema
Data schema
ROUTE
matches
DOCK
loads
STAFF
handles
Ops Stream
Stream
relations mapped in realtime
DEVICE
LOG
TIME
STATUS
LG-FL08-T2
[Fleet] utilization 87%
09:05:21
LG-FL08-T2
[Route → Dock] loading slot matched, dock 04
09:08:55
LG-WH01-D4
[Replenish] auto order drafted, approval pending
09:12:12
LG-WH01-D4
[Stock] SKU-1042 below threshold
09:12:40
LG-FL08-T2
[Dock → Staff] handling task assigned
08:58:47
LG-WH01-D4
[Inbound] pallet scanned, zone C
09:01:03
LG-WH01-D4
[Stock] SKU-1042 below threshold
09:12:40
An empty shelf is revenue walking out the door
In retail, the challenge is how long shelves sit empty.
Empty shelves found late
The only check is a staff walk-through, empty shelves go unnoticed.
Ordering blind to demand
Weekend and promo demand arrives late, so stock swings both ways.
No restock priority
No rule for which shelf comes first, it's decided by feel.
Operations that spot the empty shelf first
It's designed to link shelf, demand, and ordering in one structure, catching stockouts early and keeping restock and orders connected.
Ontology
SHELF
89%
relation coverage
Data schema
Data schema
Data schema
DEMAND
forecasts
STAFF
restocks
PRICE
drives
Store Stream
Stream
relations mapped in realtime
DEVICE
LOG
TIME
STATUS
RT-DM01-Q9
[Forecast → Order] auto draft created
11:19:52
RT-DM01-Q9
[Demand] weekend uplift forecast +18%
11:20:15
RT-ST03-A3
[Restock → Staff] task assigned, 3 min ETA
11:23:48
RT-ST03-A3
[Shelf] empty slot detected, aisle 3
11:24:09
RT-DM01-Q9
[Price → Demand] promo impact linked
11:10:22
RT-ST03-A3
[Shelf] restock confirmed, slot B4
11:15:36
RT-ST03-A3
[Shelf] empty slot detected, aisle 3
11:24:09
Delays don't just happen on the road
In last mile, the challenge is how fast you respond after a delay.
Rerouting starts too late
The detour search starts after you're stuck, and delays cascade.
No word to customers
Delay news reaches customers late, and saved time goes to inquiries.
Handoffs lose the trail
Records break at each hub transfer, so lost parcels are hard to trace.
Routes that redraw when delay signals hit
It's designed to read traffic and delivery status in real time, redrawing routes and carrying the update all the way to the customer.
Ontology
ROUTE
86%
relation coverage
Data schema
Data schema
Data schema
DRIVER
follows
TRAFFIC
affects
CUSTOMER
notified
Delivery Stream
Stream
relations mapped in realtime
DEVICE
LOG
TIME
STATUS
LM-IC03-V5
[Traffic] congestion ahead on segment 7
10:58:56
LM-IC08-Q2
[Parcel] handoff scanned, hub 2
11:01:19
LM-IC03-V5
[Delay → Customer] proactive notice linked
11:04:40
LM-IC03-V5
[Route] real-time redesign applied
11:05:12
LM-IC03-V5
[Delivery] proof captured, slot met
10:49:08
LM-IC08-Q2
[Driver → Route] path optimization matched
10:54:31
LM-IC03-V5
[Route] real-time redesign applied
11:05:12
Thinking about bringing us in?
AI Hub is NEXTPAY's AI execution platform for offline industries. It captures operational data in the field, adds relationships and meaning through Ontology, and assembles agents that know your industry's context, designed so their decisions carry through to real operations. It isn't a fixed feature list; it's a structure each site uses to design and run AI around its own way of operating.
Most tools stop at recording and reporting. They tell you what happened; what to do next stays on you. AI Hub weaves your scattered data together so AI understands the context of your operation, and is designed to point to the next action, not just the numbers.
Yes. Most AI assumes your data is already organized, offline operations rarely are. AI Hub puts collection devices where nothing is recorded, pulls from the systems you already run, and connects data your partners hold. You don't need to tidy anything up first.
AI Hub isn't built for one vertical, it's a structure that works across industries. The Ontology stays the same; only the industry-specific judgment and agents get swapped. Moving into a new industry doesn't mean rebuilding from scratch.
Not everything, by design. Which decisions run automatically and which wait for a person's sign-off is something we design together, around your site's conditions. The scope of automation stays in your hands.
Yes. Nothing gets replaced, AI Hub adds an operating layer on top of what you already run. Start in one place, prove it out, then expand.
With a diagnosis of your operation: what data is already accumulating, what systems you run, and which decisions keep passing through human hands. Then we decide together which layer to apply first. An expert who understands your field is with you from the design stage.
What we to
Turn complexity
into simple action
Better decisions from AI,
for everyone working the field.