Industries · Retail & E-commerce
Banao builds and ships retail AI on real storefronts — recommendation engines, semantic product search, demand forecasting, and dynamic pricing — for D2C brands, marketplaces, and quick commerce.
We measure each surface against the number it is supposed to move. If a homepage recommender does not lift basket value, we say so and turn it off. We sell instrumented systems, not personalization widgets.
Myntra— engineering pods on a fashion marketplace running search, discovery, and personalization at scale.
Every retail business fails at one of three things: not enough new customers, not enough repeat, or not enough margin per order. We start where your gap is, and where the number is measurable.
Recommenders that mix collaborative, content, and LLM signals on a real-time feature store, instrumented per surface — homepage, PDP, cart, search — so you ship only what moves AOV and repeat rate.
Vector search with LLM query understanding and a ranking model that returns the product a shopper means, not the keyword they typed. Cuts null-search and search-exit on large catalogs.
Forecasts at SKU and store level wired into purchasing and replenishment, so you stop choosing between stockouts on hero lines and dead capital on the rest.
Competitor tracking, demand and price-elasticity models, and a pricing recommendation dashboard category managers can override. Protects margin without ceding control to a black box.
AI agents on WhatsApp, web, and voice, wired to your order, returns, and product data, with clean escalation to a human. Deflects repetitive tickets and recovers carts in regional languages.
Return-eligibility checks, image-based damage assessment, and fraud scoring on the refund flow — aimed at the returns that quietly destroy contribution margin.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The work is real engineering on live platforms; we will not publish a number before it is verified.
Myntra
Banao engineers worked inside specific pods on one of India's largest fashion marketplaces, on the systems that decide what a shopper sees and finds — product search, discovery ranking, and personalization — at marketplace scale and traffic.
Swiggy
On one of India's largest food and quick-commerce platforms, Banao contributed to engineering pods where the hard problems are demand at the dark store, routing, and ETA accuracy — the systems that decide whether an order arrives on time and at a profit.
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.
So when we instrument your storefront, we are running the same playbook we run on our own funnel every day. The version of a recommender or a search model that reaches your PDP has already had to survive a business that depends on it.
Most personalization vendors will sell you a widget regardless of whether it moves a number. We would rather tell you when not to build — it is why growth heads take our second call.
You have been pitched a 6-month personalization rollout before. We start by proving where the lift is, per surface, not by quoting a platform build.
We instrument your funnel and rank the AI plays by the P&L gap they close — conversion, AOV, or repeat rate. You walk out with a prioritised list, baseline lift maths, and a go/no-go per surface, yours to keep either way. If you proceed, the Sprint cost is credited against the build.
Data unification first, then the model. We wire into your storefront, CRM, catalog, and order systems — Shopify, headless, or custom — and ship v1 of a recommender or search in weeks, with Core Web Vitals as a gate, not an afterthought.
We ship each surface behind an A/B test and keep only what moves the number. The models retrain on live behaviour, and your team owns the codebase from day one.
Shopify works fine for us. Why would we need custom AI?
Shopify is the right call early. Most brands hit its ceiling around a few million in annual GMV — where custom flows, real personalization, or multi-region need outpace what plugins do. We are not anti-Shopify; we add an AI layer to what you have, and only replatform if the math says so.
We tried personalization and saw no lift. Why is this different?
Most personalization fails because it is bolted on as a widget, not integrated into the funnel. We measure lift per surface — homepage, PDP, cart, search — and only ship what moves the number. If a surface does not move it, you do not pay to keep it on.
We don't think we have enough data for AI. Are we out?
Probably not. Most brands underestimate their own data because it is fragmented across the store, CRM, and support tools. Step one of any engagement is unifying it. Once we see the full picture, the AI plays that actually pay back surface on their own.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, you keep the lift model whether or not you continue. Worst case you have a free assessment of where AI pays off; best case you have your board business case.
How fast can a recommender or search model reach our site?
A typical path is a 2-week Sprint, then a 6–8 week build to v1 in production behind an A/B test, then weekly iteration. Banao's ~300-engineer bench means delivery starts in weeks, not the quarters a single in-house hire would take.
Bring your conversion, AOV, or repeat-rate gap. In 45 minutes we'll map the AI play that hits it directly and the lift maths behind it.
Book a 45-min scoping call
