Industries · Logistics & Supply Chain
Banao builds and deploys AI across live logistics operations — route and last-mile optimization, shipment ETA prediction, warehouse automation, and freight document processing — for 3PLs, fleet operators, and distributors.
Every system below is running against real orders, fleets, and TMS/WMS data. We sell deployed operations software, not a forecasting notebook.
Swiggy— routing and ETA models running across a live last-mile delivery network.
Each of these is a problem with a rupee or dollar attached — fuel, idle assets, penalties, or data-entry labour. We start where the cost is measurable.
Routing models that cut empty miles and missed windows against live traffic, order, and vehicle constraints — with a driver app and dispatcher override on every plan.
Models over GPS, traffic, and historical lane data that give customers and ops an arrival time they can plan around, and flag at-risk shipments before they slip.
AI-driven slotting, cycle-count automation, and picker guidance wired into your WMS, so inventory accuracy and pick rate stop depending on the most experienced shift.
Document AI that pulls structured data from BOLs, invoices, and customs forms into your TMS, with a 3-way match and an exception queue in place of a keying team.
Forecasting and replenishment models wired into your ERP that hold service levels without parking cash in dead stock or burning it on expedited freight.
Sensor and telematics models that flag vehicle failures before a breakdown strands a load, with a maintenance schedule your fleet team actually works from.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we will not publish a number before it is verified.
Swiggy
Swiggy runs hyperlocal delivery at a scale where a few minutes per order compounds into millions. Banao works on routing, dispatch, and ETA models that hold delivery promises against live traffic and order surges, with dispatcher override kept on every assignment.
Indian Oil
Indian Oil moves fuel across one of the country's largest distribution networks, where a depot stockout and an idle tanker both cost real money. Banao applies forecasting and fleet-routing models over existing demand, depot, and movement data — adding a planning layer on top of the systems already in place rather than ripping them out.
Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our own hires. Vikaas runs our own demand generation.
That is the difference between a vendor who has read about operations AI and one who runs on it daily. By the time a routing or document model reaches your network, it has already had to survive ours.
Most AI vendors will sell you a model regardless. We would rather tell you when not to build — it is why operations heads take our second call.
You have been pitched AI by five vendors already. We start by proving the cost of the problem, not by quoting a build.
On-site at your depot or warehouse if needed. You walk out with a prioritised list of AI opportunities, baseline ROI maths, and a go/no-go per opportunity — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
Data engineering first, then the model. We build the cleaning pipeline as a deliverable and integrate with your TMS, WMS, and ERP — legacy EDI feeds and scanned paper included.
Deployment with dispatcher and planner override and a dashboard, plus change management for the ops floor. The model keeps improving with each day's orders and routes.
Our routing lives in one dispatcher's head. Does that rule us out?
No — that is the usual starting point. The first job is to get the network, lanes, and constraints out of one person's head and into data the model can use. We run that capture in week one, and the dispatcher keeps an override on every plan the system produces.
We don't have clean tracking data. Can we still start?
Yes. Almost no fleet has clean data. We need some signal — scans, GPS pings, status updates — not perfect telematics. The first two weeks of any engagement is data engineering, and the cleaning pipeline is part of the deliverable, not a prerequisite.
We tried a TMS optimization module and it failed. Why is this different?
Off-the-shelf modules assume your network looks like the vendor's template. We build against your actual lanes, carriers, and constraints, and include change management for dispatchers and drivers — adoption is where most routing projects die, so we treat it as a deliverable.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, you keep the ROI model whether or not you continue. Worst case you have a free assessment; best case you have your board business case.
How fast can a system reach operations?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week rollout across depots. Banao's ~300-engineer bench means delivery starts in weeks, not the months a local hire would take.
Bring your worst lane, your idle fleet, or your freight-document backlog. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a 45-min scoping call
