Logistics & Supply Chain · Warehouse automation
Banao builds AI warehouse automation that optimises pick paths, slot assignments, dock schedules, and task orchestration in real time — so throughput climbs without adding headcount.
The system integrates with your existing WMS, ERP, and scanning hardware. It does not require a warehouse rebuild or a new platform licence: it works with what you have today.
Warehouse AI is not a single model. It is a set of coordinated decisions — slotting, routing, scheduling, labour — each needing its own logic and its own data feed.
SKUs placed by velocity, co-pick frequency, and physical constraints — cutting average pick-walk distance and reducing the re-slotting cycles that tie up supervisors.
Dynamic pick sequences generated per order and per aisle state, so pickers travel the shortest viable route rather than the sequence the WMS printed at shift start.
Inbound dock windows allocated by yard state, offload crew availability, and put-away backlog — cutting truck dwell time and the congestion that delays outbound.
Tasks assigned to the right person at the right time based on skills, location in the warehouse, and queue depth — not a printed pick sheet that ignores live floor state.
Forward-pick locations replenished before they run dry, and reorder signals generated off consumption rate and lead-time data rather than periodic cycle counts.
Shift-level visibility of pick rates, dock utilisation, task completion, and exception queues — so managers act on decisions rather than chase numbers.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Swiggy
Swiggy's dark-store model demands sub-10-minute fulfilment at high order density. Banao deployed pick-path and task-routing logic that sequences orders against live floor state, cutting assembly time and mis-picks simultaneously.
Banao operates a ~300-person engineering company on its own AI products. InterviewGod makes every hiring shortlist decision before a human reviews candidates; Vikaas drives our demand-generation pipeline without a traditional SDR team.
We have been on the receiving end of operations AI every working day for years. That experience is what we bring to a warehouse engagement — not a demonstration, not a reference architecture.
Warehouse AI compounds good data. It does not compensate for broken data or unstable operations. We will say so before you start:
Every warehouse AI engagement starts with the same question: where is the throughput actually going? We answer that question before writing a line of model code.
We audit your WMS transaction logs, walk the floor, and map actual pick-walk distances, dwell times, and mis-pick rates against your current slotting logic. The output is a ranked loss inventory and ROI estimate for each fix — yours to keep whether you build with us or not. If you proceed, the Sprint fee is credited against the build.
Model development, WMS and ERP integration, and an operations dashboard. Slotting, routing, and task-orchestration modules are delivered independently so you can go live in phases without waiting for the full system.
Go-live support, floor-team onboarding, and a feedback loop that retrains models on live transaction data as SKU mix and order patterns shift.
What WMS and ERP systems can you integrate with?
Banao integrates with SAP WM and EWM, Oracle WMS, Manhattan Associates, Blue Yonder, and custom-built warehouse management platforms. We read transaction logs and, where access allows, write back task assignments and slot updates via API or direct database connection. The Discovery Sprint maps what your WMS actually exposes before we commit to scope.
Does this require new hardware — conveyors, AGVs, or RFID?
No new hardware is required as a starting condition. Banao's warehouse AI optimises the decisions made by the people and equipment already in your facility. New hardware can be layered in later where the data shows it will pay back, but it is not a prerequisite.
How long before throughput improves?
Slotting changes typically show measurable improvement within the first full reslot cycle — often two to four weeks after go-live. Pick-path routing improvements are visible on the first shift it runs. Dock scheduling results depend on data quality and yard size, typically visible within a month.
What happens when the SKU mix or order profile changes?
Models are retrained on live transaction data at a cadence you set — weekly for fast-moving assortments, monthly for stable ones. The operations dashboard surfaces model drift signals so you can trigger retraining before throughput is affected.
How do warehouse staff interact with the system?
Pickers receive optimised sequences on their existing scanners or mobile devices — the interface does not change. Supervisors see task queues and exception flags on the dashboard. Staff can override any recommendation, and overrides feed back into the model so it improves from floor-team corrections over time.
Bring your WMS export, your mis-pick log, or a description of the shift that never closes on time. In 45 minutes we will tell you whether warehouse AI closes that gap — and what the ROI maths look like.
Book a 45-min scoping call
