Retail & E-commerce · Visual search
Your catalogue is invisible to the shopper who searches by photo
Banao builds AI visual search that takes a product photo — from a shopper's camera roll, a screenshot, or a competitor page — and returns ranked catalog matches with attribute filters in real time.
It runs on your existing product catalog, no re-platforming required. The image embedding index is refreshed on each catalog update, so new arrivals surface in search the same day they go live.
Myntra— visual search integrated into catalog discovery for fashion and accessories.
The first call is free · 45 minutes · no obligation
What we build
What a Banao visual search deployment covers
A production visual search is more than a similarity model. It is the embedding pipeline, the attribute layer, the mobile integration, and the analytics — we own all of them.
Catalog embedding and image index
Every product image is encoded into a high-dimensional vector and indexed for sub-second approximate-nearest-neighbour retrieval. Index refreshes trigger on catalog sync, not on a nightly batch.
Multi-modal re-ranking
After visual similarity retrieves candidates, a second pass re-ranks by applied filters — size availability, price band, colour preference — so the top results are both visually close and shoppable.
Attribute extraction from images
Neckline, sleeve length, material, pattern, silhouette — the model tags each product automatically. Shoppers refine by attribute without needing the catalogue team to hand-label everything.
Mobile camera and screenshot ingestion
Search entry points: camera roll upload, live camera crop, URL paste, and in-app screenshot. Each normalises to the same query pipeline, so the UX team picks entry points without touching the model.
Cold-start handling for new catalog arrivals
New products embed and index within minutes of going live. There is no overnight batch delay, and the model handles products with only one hero image before user-generated photos exist.
Search analytics and conversion telemetry
Which visual queries find nothing, which find the wrong thing, and which convert — tracked per query and per category. The data drives both model fine-tuning and category manager decisions.
Receipts
Where this is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Visual search embedded into fashion catalog discovery
Shoppers searching by saved screenshot or camera image couldn't land on the right product through text. Banao built an image embedding pipeline across the live catalog and integrated visual query entry into the existing search UX with attribute-based re-ranking.
Dogfooding
We depend on our own AI before you do
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens our own engineering hires; Vikaas runs our own pipeline generation. A model that has to survive internal scrutiny is already hardened before it ships.
We are not advising on production AI from the outside. We run it every working day — which is the standard we hold every catalog search model to before it touches your shopper experience.
Screens Banao's own engineering hires each week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When visual search does not move the needle
Visual search adds real value in specific product categories. We will tell you before you build whether yours qualifies:
- Commodity categories: if your catalog is printer cartridges or fasteners, shoppers already search by SKU and spec. A visual model adds no discovery value there.
- Thin catalogs: below a few thousand SKUs, a curated text taxonomy usually outperforms image retrieval. We will model the break-even point before the Discovery Sprint completes.
- Low-quality product imagery: if catalog photos are inconsistently lit, cropped by different photographers, or missing secondary angles, the model's accuracy ceiling is set by the image quality — not the algorithm. We audit your catalog before quoting.
How we start
How we start — prove it on your catalog first
We do not quote a visual search build from a spec sheet. We test it on your actual catalog images first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We embed a representative slice of your catalog, run benchmark queries against your hardest search failure cases, and return a retrieval-accuracy baseline and realistic conversion-lift estimate — yours to keep whether or not you proceed. If you do, the Sprint fee is credited to the build.
- 02
Build
Full embedding pipeline, attribute extraction, multi-modal re-ranking, mobile camera integration, and analytics telemetry — integrated into your catalog data pipeline and storefront search entry points.
- 03
Production & continuous improvement
Catalog refresh automation, zero-result monitoring, model fine-tuning on conversion signals, and quarterly accuracy audits. The model improves as your catalog grows and shopper behaviour data accumulates.
FAQ
Frequently asked questions
What catalog size is needed for visual search to work?
There is no hard floor, but retrieval quality improves with catalog depth. A catalog under roughly 5,000 SKUs often gets better discovery ROI from a well-structured text taxonomy. The Discovery Sprint models the break-even for your specific assortment mix.
Does it handle mobile camera search, not just image uploads?
Yes. Banao integrates live camera, camera-roll upload, and screenshot paste as query entry points. Each feeds the same retrieval pipeline — the UX team controls which entry points appear in the app without touching the model.
How does visual search interact with our existing text search?
They run as parallel retrieval paths that merge at re-ranking. A shopper can refine a visual query with text filters, or a text query can fall back to visual similarity when it returns zero results. The integration sits above both indexes.
How does the model keep up with new catalog arrivals?
New product images embed and index within minutes of the catalog sync event, not on a nightly batch. There is no delay window where a live product is invisible to visual search.
How long does a build typically take?
A standard build — embedding pipeline, attribute extraction, mobile integration, and storefront wiring — runs 8–12 weeks depending on catalog data readiness and existing search infrastructure. The Discovery Sprint sets the exact scope and timeline before any build commitment.
Get started
Test visual search against your hardest search failures
Bring your worst zero-result queries and a sample of your catalog. In 45 minutes we will tell you whether visual search will close that gap — and what it would cost to build it.
Book a Discovery Sprint