People and AI,
deciding as one.
“It flags the rush before it hits, so prep starts earlier.”
Bistro chef-owner
“We see surges coming and stage riders ahead of time.”
Delivery agency branch head
“It calls the reorder moment first — stock-outs stopped haunting us.”
Grocery mart owner
“Routes re-plan themselves in real time — delays plummeted.”
Last-mile hub manager
“Inbound and outbound on one screen — bottlenecks fix fast.”
Logistics center director
“Attendance links to achievement — counseling got deeper.”
Academy director
“Staffing the peak hours got so much easier.”
Restaurant manager
“We re-planned the aisles from cart-path data.”
Retail merchandiser
“It flags the rush before it hits, so prep starts earlier.”
Bistro chef-owner
“We see surges coming and stage riders ahead of time.”
Delivery agency branch head
“It calls the reorder moment first — stock-outs stopped haunting us.”
Grocery mart owner
“Routes re-plan themselves in real time — delays plummeted.”
Last-mile hub manager
“Inbound and outbound on one screen — bottlenecks fix fast.”
Logistics center director
“Attendance links to achievement — counseling got deeper.”
Academy director
“Staffing the peak hours got so much easier.”
Restaurant manager
“We re-planned the aisles from cart-path data.”
Retail merchandiser
“It flags the rush before it hits, so prep starts earlier.”
Bistro chef-owner
“We see surges coming and stage riders ahead of time.”
Delivery agency branch head
“It calls the reorder moment first — stock-outs stopped haunting us.”
Grocery mart owner
“Routes re-plan themselves in real time — delays plummeted.”
Last-mile hub manager
“Inbound and outbound on one screen — bottlenecks fix fast.”
Logistics center director
“Attendance links to achievement — counseling got deeper.”
Academy director
“Staffing the peak hours got so much easier.”
Restaurant manager
“We re-planned the aisles from cart-path data.”
Retail merchandiser
AI Hub reads your scattered operations data in context,
then hands each site the decision — and the next move.
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.
Knows the domain.Works with people.AI Agent, built to decide.
The rush doesn't runon a schedule
Sales, orders, and inventory live in different places — so the data never turns into decisions.
In F&B, the challenge is how fast you decide at peak time.
Order volume shifts daily with weather, weekdays, and events — with nothing to predict from, prep runs on the manager's experience.
Sales land in the POS, stock deductions in another system — no one knows what's actually left, in real time.
There's no data showing when the rush will hit, so scheduling leans on the manager's 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.
Event StreamStream
relations mapped in realtime
Order surges never comewithout warning
The signals are already in the data — but dispatch and prep always start one beat late.
In delivery, the challenge is being ready before the surge.
Spikes get confirmed after they've passed, so securing riders and starting prep always begins late.
Cook-finish and rider-arrival times drift apart — food goes cold, or riders stand around.
Without a real-time read on demand by zone, some zones sit on idle riders while others run short.
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.
Order surge forecast
92%
Rider pre-positioning
ontology-grounded suggestion
zone 4, +3 riders ▸
Optimal prep time
ontology-grounded suggestion
ETA-linked, 12 min ▸
3,924 DISPATCH DECISIONS TODAY
Darker cells mark the hours where dispatch suggestions concentrated.
Dispatch StreamStream
relations mapped in realtime
Stockouts don't start in the warehouse —they start in the structure
Inventory, fleet, and dock each look fine on their own. Stockouts and idle time happen when they move without knowing each other.
In logistics, the challenge is moving before the stockout.
Stock below threshold gets discovered at outbound — which means emergency orders and extra cost.
Truck arrivals and dock slots are managed separately, so waiting piles up on both sides.
Replenishment drafts pass through hands after hands, and lead time stretches with every pass.
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.
Fleet utilization
87%
Replenish before stockout
ontology-grounded suggestion
SKU-1042, auto order drafted ▸
Loading slot matched
ontology-grounded suggestion
route → dock 04, 14:20 ▸
Human approval point
ontology-grounded suggestion
manager approval pending, 1 ▸
Ops StreamStream
relations mapped in realtime
An empty shelf is revenuewalking out the door
Stockouts drag on not because the stock is gone — but because nobody knows the shelf is empty.
In retail, the challenge is how long shelves sit empty.
The only check is a staff walk-through — every minute a shelf sits empty is a sale missed.
Weekend and promo demand reaches the order sheet late, so overstock and stockouts take turns.
With no rule for which shelf to fill first, it's decided by feel — wasting steps and time.
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.
Shelf coverage
96.2%
Empty shelf detected
ontology-grounded suggestion
aisle 3, slot B4 ▸
Restock task assigned
ontology-grounded suggestion
staff 02, 3 min ETA ▸
Weekend uplift forecast
ontology-grounded suggestion
demand +18%, order drafted ▸
Store StreamStream
relations mapped in realtime
Delays don't just happenon the road
As long as routes stay fixed and blind to conditions, one delay becomes a chain of them.
In last mile, the challenge is how fast you respond after a delay.
The search for a detour starts after you're already stuck — and one delay spreads to every stop behind it.
Delay news reaches customers late, and the time you saved gets spent on inquiries and complaints.
Records break at every hub transfer, so lost or misrouted parcels are hard to trace back.
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.
delay-aware rerouting
Delivery StreamStream
relations mapped in realtime
Thinking about bringing us in?
Start with these questions.
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.
What we do
Turn complexity
into simple action
Better decisions from AI,
for everyone working the field.