Retail & E-commerce · Inventory optimization
Banao builds inventory optimization systems that forecast demand at SKU and location level, translate those forecasts into purchase orders and transfer recommendations, and wire directly into your ERP or warehouse management system.
We do not sell a planning dashboard. We deliver a model that decides when to order, how much, and from where — instrumented so your buyers can override any recommendation, and calibrated against actual stockout and holding costs each month.
Swiggy— inventory replenishment and dark-store stock management for quick commerce at scale.
Most forecasting projects produce a spreadsheet nobody trusts. We build a system your buyers actually use — wired to the numbers they care about: fill rate, days cover, and working capital.
Probabilistic forecasts per SKU per store or dark store, updated daily, weighted for seasonality, promotions, and regional variation. The forecast is the input; the replenishment decision is the output.
Dynamic safety stock bands driven by actual demand variability and supplier lead-time history — not static formulas set three years ago. Updated automatically as patterns shift.
Identifies stock sitting idle in one warehouse or dark store while a stockout is forming in another, and generates inter-location transfer orders before the hole opens.
Flags SKUs trending toward overstock before they become a markdown problem, giving category managers lead time to act on promotions, returns, or liquidation before holding cost compounds.
Tracks actual delivery performance per supplier and SKU rather than assuming the quoted lead time. Order timing accounts for the gap between what a supplier promises and what they deliver.
Forecast and replenishment output connects to SAP, Oracle, Unicommerce, and custom warehouse systems via API, so a recommendation reaches a purchase order without a manual export step.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. We publish only once verified.
Swiggy
On India's largest quick-commerce platform, Banao worked on the inventory systems that decide what each dark store holds and when to replenish — where a stockout costs a fulfilled order and overstock costs delivery speed.
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. The same data-driven logic we apply to inventory decisions at client sites, we apply to our own hiring pipeline and capacity planning.
An AI system that has to survive our own business first arrives at your warehouse already tested — not in theory, but against a real operation that depends on it.
Inventory optimization models are straightforward to over-engineer and easy to undersell. We will tell you which situation you are in before you spend on a build:
We audit your current forecast error and holding-cost pattern before quoting a model. Two weeks is enough to identify the SKU clusters where an AI forecast closes the gap.
We analyse your transaction history, current reorder logic, and stockout and overstock rates by category. You get a ranked list of SKU clusters where an optimization model pays back, a baseline forecast-accuracy estimate, and the working-capital improvement maths — yours to keep. If you proceed, the Sprint cost is credited against the build.
Data pipeline, demand model, and replenishment engine wired to your ERP or WMS. Buyers see recommendations in their existing workflow, with full override and reason-code logging from day one.
The model retrains weekly on live sell-through. Buyer overrides feed back into lead-time and demand priors. Monthly reviews compare model-driven orders against actuals on fill rate, days cover, and working capital.
How much sales history do we need to start?
Generally 12 to 24 months of weekly sell-through at SKU level, with supplier delivery records. Less is workable if you have strong category-level data for hierarchical modelling. The Discovery Sprint establishes what your history supports before you commit to a build.
Can it connect to our ERP or warehouse system?
Yes. We integrate with SAP, Oracle, Unicommerce, and custom WMS platforms via API. The integration is part of the build deliverable — a replenishment recommendation that does not reach a purchase order has no business value.
Does this work for fashion and short-lifecycle products?
It works differently. Fashion needs open-to-buy models and size-curve forecasting rather than rolling demand history. The Discovery Sprint distinguishes whether your catalog needs a replenishment model, an option-planning model, or both.
How do buyers stay in control of the model's recommendations?
Buyers can override any recommendation with a reason code — promotional cover, delayed shipment, new supplier, catalogue drop. Overrides log to a dashboard and feed back into the model, so buyer knowledge improves accuracy over time instead of being overwritten by it.
What improvement in stockout rate can we expect?
That depends on your current forecast method and catalog complexity, which is exactly what the Discovery Sprint quantifies. We will not promise a number before we see your data — but you will have the improvement maths after two weeks, yours to keep either way.
Bring your current stockout rate, overstock cost, and forecast method. In 45 minutes we will identify whether an optimization model closes the gap — and what that would mean for working capital.
Book a 45-min scoping call
