Logistics & Supply Chain · Last-mile optimization
Banao builds last-mile optimization that assigns, sequences, and re-routes your fleet against live traffic, delivery windows, and vehicle capacity — without a dispatcher rebuilding a spreadsheet every two hours.
The system runs against real order data and TMS records, produces a driveable plan in under a minute, and keeps a human in the loop for overrides. Swiggy runs a variant of this pattern across its live delivery network.
Swiggy— dispatch and last-mile routing models running against live order and traffic data.
Route optimization is one problem with five dependencies — constraint handling, re-routing, driver UX, TMS integration, and dispatcher override. We ship all of them.
Orders, vehicle capacities, driver shift limits, delivery windows, and restricted zones all feed the optimizer. Plans respect every constraint before a truck moves.
When a driver is delayed, a window is lost, or a new order drops in, the model re-sequences the affected vehicles automatically and pushes the updated plan to the driver app.
Drivers receive a sequenced task list with navigation, proof-of-delivery capture, and an exception button — not a phone call from dispatch every time something changes.
Dispatchers see every vehicle's position and plan in one view, can reassign stops, and approve or reject model suggestions. The AI handles the computation; humans handle the judgement calls.
Route plans feed back into your existing TMS or order-management system — no parallel tracking system, no double entry, no manual export step.
Per-route, per-driver, and per-zone reporting on delivery performance, fuel consumption, and window adherence, so operations heads can see where the margin is going.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified against the operator's data.
Swiggy
Banao built and deployed routing and dispatch optimization that handles multi-drop sequencing against live traffic and delivery windows across Swiggy's network. The dispatcher-override layer was a non-negotiable requirement from day one.
Banao manages a ~300-person engineering operation, and every AI system we sell runs inside our own business first. InterviewGod screens our own engineering hires; Vikaas runs our own demand-generation pipeline. A system that has to survive inside a real operation is already tested before it touches yours.
When we say a last-mile model needs a human override layer and real TMS integration to survive beyond a pilot, that is not theory. We know what happens when a system is deployed without it.
Route optimization compounds on volume and constraint complexity. Below certain thresholds, simpler tools outperform a custom AI deployment:
We do not quote a fleet-wide optimization system off a discovery call. We look at your actual routes, windows, and dispatch data first.
We audit a sample of your real route and order data, model one delivery zone, and hand back a baseline plan-quality estimate and ROI calculation — yours to keep regardless of next steps. If you proceed to build, the Sprint fee is credited against the engagement.
We build the optimizer, driver app, and dispatcher dashboard for a defined zone or vehicle class. TMS integration and dispatcher override are part of the deliverable, not add-ons.
Phased expansion to the full network with monitoring, re-routing logic tuned to your lanes, and a performance dashboard operations heads check daily.
How long does it take to get a route plan from the model?
For most fleet sizes, the optimizer produces a driveable plan in under 60 seconds after order cut-off. Re-routing on a mid-day disruption is typically faster — it only re-sequences the affected vehicles, not the whole fleet.
Can dispatchers override the AI plan?
Yes, and deliberately so. The dispatcher dashboard shows the AI plan, lets dispatchers reassign stops or swap vehicles, and logs the override reason. We do not ship a system that removes dispatcher judgement — that is how pilots fail in month three.
Does this work with our existing TMS?
We integrate with TMS platforms via API or direct database connection. Where a TMS exposes no API, we build a middleware layer. The Discovery Sprint includes a TMS integration assessment, so you know the effort before committing to the build.
What if our network is hyperlocal versus long-haul?
The optimization model differs: hyperlocal (sub-30-minute, high drop density) uses different constraint weighting than intercity or long-haul routes. We scope the right solver for your network in the Discovery Sprint rather than applying a one-size-fits-all engine.
How do you handle driver app adoption?
Driver onboarding and change management are part of the rollout deliverable, not an afterthought. The app interface is kept to the minimum a driver needs mid-route — sequence, navigation, and a single exception button — to reduce friction. Adoption metrics are tracked from day one.
Bring your last month of route and order data. In 45 minutes we will tell you whether an optimization model would move your on-time rate, and what a realistic build would require.
Book a 45-min scoping call
