Logistics & Supply Chain · Shipment ETA prediction
Your ETA is a guess. Customers are planning their day around it.
Banao builds shipment ETA prediction that puts a real number on when a load arrives — drawn from GPS pings, historical lane times, traffic feeds, and carrier performance — then flags at-risk shipments the moment a delay becomes probable, not after the miss is confirmed.
The model feeds your customer portal, ops dashboard, and exception queue. It runs against live order and telematics data, and narrows the arrival window as the shipment moves.
Swiggy— ETA models running on a live hyperlocal delivery network, narrowing each arrival window in real time.
What a Banao ETA deployment includes
An ETA model is only as useful as the outputs it feeds. We build the model, the integration into your portal and exception queue, and the ops-team workflow around it.
A model trained on your lanes, carriers, and depots
We train on your historical order, GPS, and carrier-performance data — your lane mix, your carrier roster, and your depot patterns — not a generic freight dataset. The model learns where time disappears on your specific network.
At-risk flagging before the miss
When a shipment's live position and pace diverge from the predicted path, ops and account teams get an alert — with time to act, not a post-mortem after the delivery window has closed.
Live ETA that narrows as the load moves
Each GPS ping updates the arrival estimate. The window tightens as the vehicle gets closer, so customer-facing ETAs are accurate at the point they matter most — the final hour.
Customer portal and notification feed
Structured ETA output delivered to your customer portal, SMS, or email pipeline — so customers have a number they can plan around without calling your contact centre.
Exception queue and dispatcher view
A single view of at-risk shipments ranked by delivery-window gap, so dispatchers work the highest-impact interventions first and don't chase false alarms across a full board.
We run our own operations on the AI we sell
Banao operates a ~300-person engineering company on its own AI products before any client sees them. Vikaas runs our own demand pipeline — from lead sourcing through to a booked call — and InterviewGod screens every engineering hire.
When we say an ETA model runs on live data, we mean we have shipped AI into operating systems where a wrong prediction has a cost attached. That is what we build for clients.
- VikaasRuns Banao's own demand generation — lead sourcing through booked call.
- InterviewGodScreens every Banao engineering hire before a human interview.
When ETA prediction doesn't earn its keep
Most vendors will build a model regardless of whether the signal justifies it. We would rather tell you when the data doesn't support a reliable ETA than sell you a number that's no more accurate than a static schedule.
- No tracking signal: if vehicles have no GPS, scan events, or status updates at all, week one is instrumentation, not modelling. That is a different scope and a longer timeline.
- Too few historical runs: below a few thousand completed shipments per lane, the model has too little history to beat a lane-average heuristic. We'll audit your data volume in the Sprint and tell you what accuracy level your current history supports.
- Highly unstructured networks: if carriers rotate every week and lanes reshape constantly, a model trained on last month's data rots before it pays back. We'll flag that in the Discovery Sprint and recommend the right starting point.
How we start — prove the signal before building the model
We don't quote an ETA system off a spec sheet. We audit your actual GPS and order data first, so you know what accuracy is achievable before committing budget.
- AI Discovery Sprint2 weeks · fixed price
We ingest a sample of your historical order, GPS, and carrier data, test ETA-signal strength on your top lanes, and hand back a feasibility report and ROI model — yours to keep whether or not you continue. If you proceed, the Sprint cost is credited against the build.
- Build
Data engineering first: GPS ingestion, lane-history cleaning, and carrier-performance features as a deliverable. Then the ETA model, at-risk flagging logic, and integration with your TMS, customer portal, and notification pipeline.
- Production & continuous improvement
Deployment with ops-team override, exception-queue integration, and a dispatcher view. The model retrains on each completed shipment so accuracy improves as your lane history grows.
Frequently asked questions
Our tracking is GPS only — no WMS scan events. Is that enough?
GPS is the primary signal, so yes — that is enough to start. WMS scan events improve accuracy at origin and destination, but the lane-history and traffic model runs on GPS position and pace alone. The Discovery Sprint maps exactly what accuracy your current signal supports.
How often does the ETA update?
On every GPS ping — typically every 30 seconds to 2 minutes depending on your telematics provider. The model recalculates on each position update, and the customer-facing ETA narrows as the vehicle closes in on the delivery point.
We use multiple carriers with different tracking APIs. Does that add to scope?
Yes, but it is expected — most 3PLs and distributors run mixed carriers. Normalising the tracking feeds into a single position stream is the first data-engineering task. We have integrated carrier APIs, EDI position updates, and manual check-call data into a unified ingestion layer. The scope is knowable in week one.
Can the model feed our customer portal directly?
Yes. The ETA output is a structured feed — an arrival estimate and confidence band per shipment, updated on each ping. We integrate that into your portal API, SMS gateway, or email pipeline as part of the build deliverable. The model doesn't add value sitting behind a dashboard your customers can't see.
How do we handle unplanned events — breakdowns, route changes, held at customs?
The model detects deviation from the expected path and flags the shipment as at-risk. For events without a GPS signal change — a customs hold or a breakdown logged by a driver — the exception queue picks up the manual status update and the ETA is recalculated. No model eliminates surprises; ours gets the intervention to your ops team while there is still time to act.
Show us your worst lane. We'll show you what the ETA model would have predicted.
Bring 12 months of completed shipment data and your current tracking setup. In 45 minutes we'll map the ETA-accuracy opportunity and the penalty or customer-trust cost behind it.
Book a 45-min scoping call