Real-time
Rider reassignment, not fixed morning-of assignments that stay put whether they should or not.
AI for same-day and express delivery
Every delivery, reassigned to the closest available rider the instant it matters - not stuck with whoever picked it up first.
Iyerxpress optimises routes across your whole rider fleet in real time, predicts realistic delivery windows, and reallocates orders dynamically as conditions change - keeping same-day promises actually kept.
The shape of the problem
Same-day delivery breaks in three familiar places.
None of them are route-planning problems in isolation. They are density and reallocation problems - the swarm has to move together.
01 - OVERLOAD
One rider buried. Another idle two streets away.
A batch lands, orders pile onto whoever is technically nearest at that moment, and the fleet ends up lopsided. Someone is running 40 minutes late while a rider three blocks away has nothing on their queue.
02 - PROMISE
A delivery window that was never realistic.
Flat estimates ignore actual traffic, rider load, and pickup queues. The customer gets promised a 30-minute window that the operation could not have kept even on a good day.
03 - STUCK
A rider goes down and their route sits.
Breakdown, wrong turn, unreachable phone. Without an automatic fallback, those orders just wait until a dispatcher notices and manually re-slots them one by one.
How Iyerxpress works
Four things happen, continuously, across your whole fleet.
Not a route-planner run once at the start of a shift. A live loop, running against every order, every rider, every minute.
01 - ASSIGN
Route to the best rider, not the nearest one
Assignment weighs location, current load, pickup queue at origin, and rider skill for the parcel type - so a technically-nearest rider does not get overloaded.
02 - PREDICT
Windows built from what will actually happen
Delivery times are estimated from live road conditions, rider workload, and historical performance on that corridor - not a flat promise the operation cannot keep.
03 - REALLOCATE
Move the order the moment a faster path opens
If a closer rider frees up mid-shift, or a rider stalls, the engine reassigns the order automatically and rebuilds both routes - before the window is actually at risk.
04 - NOTIFY
Everyone kept honest, without a dispatcher chasing
Customers get a live tracking link and status changes as they happen. Dispatchers get a board view; managers get on-time, utilisation, and window-accuracy trends.
What the AI does, specifically
Not a generic optimiser. Five concrete capabilities you can point at.
Each of these has a dedicated page in the delivery engine, with worked examples and honest notes on current coverage and accuracy.
Real-time route optimisation
Rider paths continuously recalculated as new orders drop in and conditions change - not a static plan set at the start of the shift.
Route engine
Delivery-window prediction
Realistic windows estimated from live traffic, pickup queues, rider workload, and historical corridor times - not a flat 30-minute promise.
Window model
Dynamic rider reallocation
Orders reassigned automatically the moment a closer or less-loaded rider becomes the faster option - before the window is at risk.
Reallocation
Automated status and tracking
Customer-facing live tracking links and status updates go out automatically. Dispatchers see the same board without manually paging riders.
Visibility
Two shapes of delivery operation
Built for both sides of the last-mile.
The same engine, tuned for two operational shapes - one dispatching for many clients at once, one dispatching for a single brand or platform.
For courier companiesRead the courier-company setup
Many clients, one live board.
Handle a mixed rider fleet and dozens of clients' orders in one dispatch layer - each client keeps their own tracking view, SLA rules, and branded status page.
Multi-client separation with shared rider pool
Per-client SLA and pricing rules
Branded tracking pages for each client
For last-mile fleetsRead the last-mile setup
One brand. Tuned to your geography.
Dedicated optimisation for your own delivery fleet - trained against your volume patterns, your service area, your window promises, without pooling with other operators.
Single-brand tracking and rider app
Corridor-level tuning to your area
Custom SLA and escalation flows
What actually changes
Three shifts you should expect within the first month.
Realistic
Delivery windows built from live traffic and current load - promises that the operation can actually keep.
Automated
Customer status updates and dispatcher visibility, without manual paging or spreadsheet reconciliation.
The delivery-engine pages carry more specific figures from pilot operations. Any hard percentage or minute value shown to you in a demo will be tied to a comparable fleet, not a generic marketing figure.
Your fleet, illustrated
Show us your fleet size. Watch the swarm move at your scale.
Type a rider count and a rough daily order volume. The view on the right rebuilds to that scale, with a sample reassignment playing every few seconds - the same kind of decision the delivery engine makes for a live fleet.
An illustrative view based on typical patterns - not a live read of your actual fleet. Numbers move with your inputs so you can see the shape of the problem the engine solves.
Data handling, retention and access model - all documented on the security page.
Read the security overview