AI for eCommerce

Every visitor gets
their own store.
Automatically.

AI-powered personalization for Shopify and WooCommerce stores - product recommendations, conversational AI search, automated merchandising, and demand forecasting. Built on your actual store data, not generic rules. Higher AOV, lower browse abandonment, less manual merchandising work.

Personalised
Recommendations per visitor
Live data
Real-time catalogue sync
Automated
Merchandising on autopilot
Personalised
Per-visitor, not per-segment
Live catalogue
Syncs with Shopify/Woo
Self-improving
Learns from behaviour
No app fees
Built native, not rented
Where Stores Leave Money on the Table

Every store shows the same products to every visitor.

A first-time visitor and a repeat buyer of premium products see the exact same homepage. That is a personalization gap most stores never close.

Same homepage for every visitor

A repeat buyer who only purchases premium sarees sees the same "Best Sellers" grid as a first-time visitor browsing budget kurtis. No relevance means lower engagement.

Keyword search misses obvious matches

A customer searches "party dress for wedding" and gets zero results because no product title contains that exact phrase - even though 40 products would fit perfectly.

Manual merchandising eats hours every week

Someone on the team manually reorders collection pages, updates "Trending Now" sections, and picks cross-sell products by gut feel - hours that could be automated.

Stockouts and overstock from guesswork

Reordering decisions are based on last month's sales and instinct. Bestsellers go out of stock during peak demand while slow movers pile up in the warehouse.

Generic abandoned cart emails

Every abandoned cart gets the same discount code email regardless of what was in the cart, browsing history, or purchase intent signals.

Cross-sell and upsell left on the table

A customer buying a phone case never sees the screen protector that most past buyers also bought. Manual "related products" curation misses obvious pairs.

What We Build

Six AI capabilities for eCommerce stores.

AI Product Recommendations

Collaborative filtering plus content-based matching - "customers also bought," "complete the look," and "similar items" sections that update in real time from live catalogue and behaviour data. Native integration, no monthly per-recommendation app fees.

Conversational AI Search

Search that understands intent, not just keywords - "party dress for a wedding" or "gift for my mom who likes gardening" return relevant results, not zero matches. Built with vector embeddings over your product catalogue.

Automated Merchandising

Collection pages that auto-sort by real-time conversion rate, inventory level, and margin - not manual drag-and-drop. "Trending Now" and "Back in Stock" sections update themselves. Hours of manual merchandising work removed weekly.

Demand Forecasting

Predictive inventory models using historical sales, seasonality, and trend signals - flags what to reorder before stockouts and what to discount before overstock becomes dead inventory. Especially valuable ahead of festive and sale seasons.

Personalised Email & Retargeting

Abandoned cart and browse abandonment flows that reference the actual products viewed, with AI-selected complementary product suggestions instead of a generic discount blast. Integrated with Klaviyo or your existing ESP.

Smart Cross-Sell & Upsell

Cart-page and post-purchase upsell recommendations driven by actual co-purchase data - not manually curated "related products." Bundled offers surfaced at the moment of highest purchase intent.

Tech Stack

Built on proven infrastructure.

Claude / GPT-4 API Pinecone / Qdrant (vector search) Shopify Storefront API WooCommerce REST API n8n / Flowise Klaviyo Python / scikit-learn
How We Work

Catalogue audit first, AI second.

1
Catalogue & data audit

We assess your product data quality, tagging structure, and behavioural data availability (GA4, Shopify analytics). AI recommendations are only as good as the data behind them.

2
Priority use case selection

Based on the audit, we identify the highest-ROI starting point - usually product recommendations or search, since both compound quickly.

3
Build and test against real data

Native integration into your Shopify or WooCommerce store. Tested against your actual catalogue and traffic before full rollout.

4
Launch and monitor

Live rollout with before/after AOV and engagement tracking. Monthly performance review and model refinement included in the first 60 days.

Pricing

Fixed-price builds, no per-recommendation fees.

Product Recommendations
Rs 40K - 90K
3-4 weeks

"Customers also bought," complete the look, and similar items - native integration into your store.

Most Common
AI Search + Recommendations
Rs 80K - 1.8L
5-7 weeks

Conversational search and product recommendations together - the two highest-impact use cases combined.

Full AI Personalization Suite
Rs 1.8L+
8-14 weeks

Recommendations, search, automated merchandising, forecasting, and personalised email - full suite.

Excl. GST. Third-party API costs pass through at cost. Get a free store audit.

FAQ

Questions about AI for eCommerce.

Get a Free Store Audit
How much catalogue data do we need for this to work well?
Recommendation engines start showing useful results with as few as 50-100 SKUs and a few weeks of purchase data. Below that, we typically recommend rule-based recommendations (manually curated but still dynamic) until enough data accumulates. We assess this honestly during the catalogue audit.
Will this replace our existing Shopify recommendation app?
Usually yes - most Shopify recommendation apps charge monthly fees for functionality we build natively into your theme, with the added benefit of being tuned to your specific catalogue and customer behaviour rather than a generic algorithm.
How is this different from Shopify's built-in "related products"?
Shopify's native related products use basic tag matching. Our recommendations combine collaborative filtering (what similar customers bought), content-based matching (product attributes), and real-time behavioural signals - a meaningfully more accurate result set that improves over time.
Does this slow down our store?
No - recommendation and search results are precomputed and cached, not calculated live on every page load. We test Core Web Vitals before and after every deployment to confirm no speed regression.

See what AI personalization could do for your AOV.

Free store audit - we estimate the impact before you commit to anything.