AI for Retail & E-Commerce

For collaborative filtering (users-who-bought-X-also-bought-Y), you need at least 1,000–5,000 purchase transactions with reasonable product and user diversity. For new stores with limited data, we start with content-based recommendations using product attributes (category, brand, color, price) and transition to collaborative models as behavioral data accumulates. We also offer hybrid approaches that combine both from day one, so you get useful recommendations immediately.

Yes — without re-platforming. For Shopify, we build custom apps using the Storefront API and embed AI widgets (recommendations, search, chat) directly into your Liquid theme. For Magento, we develop extensions and integrate via GraphQL/REST APIs. For Odoo E-commerce, we have deep native integration capabilities. In all cases, AI runs as a microservice behind your storefront, so there’s zero impact on your site’s page load speed.

New products with zero purchase history are a classic challenge. We solve it multiple ways: content-based similarity (match new products to existing ones by attributes), visual similarity (use computer vision to find lookalike products), trending signals (early engagement data from views and cart-adds), and editorial rules (manual merchandising overrides during launch periods). Most new products start surfacing in recommendations within 24–48 hours of going live.

Based on industry benchmarks and our experience: product recommendation engines typically lift revenue by 10–30% (Amazon attributes 35% of revenue to recommendations). Dynamic pricing can improve margins by 5–15%. Demand forecasting reduces stockouts by 30–65% and overstock by 20–30%. Conversational AI chatbots reduce customer service costs by 40–60%. We set up A/B tests for every deployment so you can measure exact lift against your baseline.

Absolutely. We build visual search systems using deep learning models (CLIP, ResNet, custom embeddings) that let customers photograph any product and find similar items in your catalog. We also build “shop the look” features that decompose outfit images into individual purchasable items, and visual merchandising tools that auto-tag products with attributes (color, pattern, style, material) for better search and filtering.

A recommendation engine PoC can be delivered in 6–8 weeks for $20,000–$40,000. A full-scale personalization platform with search, recommendations, and customer analytics takes 12–20 weeks and ranges from $80,000–$300,000. Our India delivery center provides 40–60% cost savings vs US firms. We always recommend starting with a focused PoC on your highest-impact use case (usually recommendations or search) to prove ROI before expanding.

Yes. We build unified AI systems that work across your DTC website, mobile app, email, push notifications, and marketplace channels (Amazon, eBay, Walmart). Product recommendations, pricing intelligence, and inventory optimization can be synchronized across all channels through a central API. This ensures consistent personalization whether a customer shops on your site, your app, or a third-party marketplace.

Three things: (1) We already built an AI-powered chatbot and analytics dashboard for a major US clothing brand — proven retail AI delivery, not theoretical. (2) We offer Odoo E-commerce + AI integration, connecting personalization and inventory AI directly to your ERP — something Shopify-only agencies can’t do. (3) With 16+ years and 150+ clients, we bring full-stack engineering depth (frontend, backend, mobile, data) alongside AI — so we build complete commerce experiences, not just isolated models.