Logistics & Supply Chain · Route optimization
Route optimization is not a new problem — but most fleets still plan from yesterday's spreadsheet against today's traffic. Banao builds routing AI that works from live order data, real-time traffic, and vehicle constraints, then puts every plan in front of a dispatcher who can override it.
The result is fewer empty miles, fewer missed windows, and a planning process that does not stop scaling when your experienced dispatcher is on leave.
Swiggy— routing and ETA models running on a live hyperlocal delivery network with dispatcher override on every assignment.
Route optimization is only the output. The underlying work is data engineering, constraint modelling, and change management — we deliver all three.
Plans generated against actual orders, vehicle load capacity, time windows, and real-time traffic — not static maps from last week. Routes recalculate when conditions change during the day.
Toll preferences, restricted zones, driver hours, refrigerated-load limits, customer time-window commitments — we encode your actual operating rules, not a generic template.
Every route is a suggestion until a dispatcher approves it. The interface shows cost and time for each plan, lets dispatchers push a change without losing the optimization, and logs the override for model learning.
Drivers follow the current best route, not the morning plan. When a stop changes or traffic closes a lane, the app updates without requiring dispatcher intervention for every minor adjustment.
When order density shifts by day or season, the model rebalances depot assignments and delivery zones so no vehicle runs half-full while another is overloaded — a rebalancing that manual planning rarely has time to do.
Fuel, kilometres, and time-window compliance by route, driver, depot, and week — so operations managers see where cost hides in the network and what planning decisions drove it.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. We will not publish a number before it is independently verified.
Swiggy
Swiggy's hyperlocal delivery runs at a scale where a two-minute improvement per order compounds across millions. Banao works on the routing and dispatch models that hold delivery windows against live traffic and surge demand, with dispatcher override preserved on every assignment.
Indian Oil
Indian Oil moves fuel across one of the country's largest distribution networks, where a tanker running empty to a depot is a direct cost. Banao applies routing models over demand, depot inventory, and tanker movement data to reduce unproductive kilometres while maintaining depot fill commitments.
Banao operates a ~300-person engineering company on its own AI products. InterviewGod screens our engineering hires. Vikaas runs our demand pipeline from lead sourcing to booked call.
A routing model that has to survive our own operations — where a mis-scheduled engineer costs real project time — is already hardened before it reaches your fleet. We are not describing operations AI from the outside.
Not every fleet needs a routing model. We will tell you before you commit budget:
Every routing engagement begins by proving the problem is worth solving, not by quoting a system.
We review a sample of your actual routes, empty-mile data, and missed windows, then model what a routing AI would have produced on the same orders. You get baseline ROI maths, a feasibility assessment, and a go/no-go — yours to keep whether or not you proceed. Sprint cost is credited against the build if you continue.
Data engineering first: we build the order, vehicle, and traffic feed pipeline as a deliverable, then the routing model and dispatcher interface. Integration with your TMS, ERP, and driver app is part of the scope.
Live deployment with dispatcher override, daily performance dashboards, and model retraining on each day's completed routes. Dispatcher corrections and overrides feed back so the model learns your network over time.
Our dispatcher plans routes from experience. Will a model replace them?
No — and we would not build one that did. The dispatcher's local knowledge is a training signal, not a liability. Every plan the model produces goes to the dispatcher for approval, and overrides are logged and learned from. The model handles the computational load; the dispatcher handles the judgment calls.
We use a third-party TMS. Can you integrate with it?
Yes. Banao integrates with existing TMS, ERP, and WMS platforms — via API, EDI feed, or direct database connection depending on what the vendor supports. The routing model feeds plans back into your existing system rather than replacing it.
How much data do we need before the model is useful?
A few months of completed routes with order, vehicle, and timing data is enough to start. The Discovery Sprint assesses your actual data in week one and tells you what is usable and what needs cleaning — the data pipeline is a deliverable, not a prerequisite.
What does the AI Discovery Sprint deliver?
A routing feasibility assessment on your actual route data, a model of what optimization would have produced on a sample of recent orders, and ROI maths — empty-mile reduction, time-window compliance improvement, and fuel savings — with honest confidence ranges. Fixed price, two weeks, yours to keep either way.
How long from Sprint to live routing?
A typical path is a 2-week Sprint, a 6–8 week build, and a 3-week rollout across depots. Banao's engineering bench means the build starts in weeks rather than the months a recruitment cycle would take.
Bring your empty-mile data, your missed delivery windows, or the lane your dispatcher has never been happy with. In 45 minutes we will tell you whether a routing model closes that gap — and what the ROI maths look like.
Book a 45-min scoping call
