Overview – AI Recommendation Engine for eCommerce Retailers

A fast-growing eCommerce retailer (under NDA) partnered with us to improve user engagement and conversion rates on their digital storefront. Their static recommendation engine offered limited personalization often suggesting irrelevant products. We built a retail and ecommerce AI agent that analyzed real-time user behavior, past purchases, and product metadata to deliver context-aware personalized recommendations.

Our AI agent for ecommerce solution was not just about boosting sales. We aimed to create a more intuitive and enjoyable shopping experience. By understanding each user’s intent and preferences, our retail AI agent delivered curated product suggestions, improved discovery, and reduced bounce rates across both desktop and mobile platforms.

Industry

Retail

Services

AI Model Development

Our Process – Building a Real-Time AI Agent for eCommerce

Our AI agent development company followed a user-centered development lifecycle focused on behavior and context.

Data Collection & Enrichment

We collected clickstream data, purchase history, product views, cart additions, and product metadata (category, price, reviews, etc). Data was cleaned. User sessions were reconstructed and product attributes were normalized for similarity mapping.

User Behavior Modeling

We developed user profiles based on browsing patterns, time-on-page, category preferences, and intent signals. Embedding techniques like Word2Vec were used to understand product similarity. Collaborative filtering helped detect shared interests among user groups.

Model Training- Hybrid Recommendation Engine Using AI

Our team implemented a hybrid recommendation system combining collaborative filtering with deep learning models (LSTM + attention) to capture user-product sequences. The model predicted the next likely products a user would interact with and refreshed recommendations dynamically.

Real-Time Agent Integration – Deploying AI Agents for eCommerce Platforms

The recommendation agent was deployed as a containerized service using Docker and orchestrated with Kubernetes. It delivered personalized results via REST API calls, integrated into the client’s frontend. Redis caching was used for speed and model outputs refreshed every 15 minutes.

The Problem – Limitations of Traditional Recommendation Systems in Retail

The existing system showed generic and outdated suggestions. The recommendations were often based solely on top-selling or recently viewed products, which didn’t align with individual customer preferences or real-time behavior. The legacy system couldn’t adapt to new customer journeys or changing user interests over time. The retailer needed a dynamic solution. So, we built an ecommerce AI agent that could learn from every click, scroll, and session to generate timely and relevant product suggestions for each unique shopper.

Our Role – End-to-End AI Agent Development for Online Retail

Developed AI Agent

Processed Data

Optimized Models

Integrated Solution

Project Challenges – Solving Real-Time Personalization and Cold Start in eCommerce

Real-Time Personalization

Delivering recommendations with sub-second latency during high-traffic hours required backend optimization and parallel processing with GPU acceleration.

Cold Start Problem

We faced the challenge of recommending products to new users with no prior data. To overcome this, we implemented a content-based fallback model that used product metadata to suggest relevant items.

Results – AI-Powered Retail Recommendations That Deliver Business Impact

The AI agent transformed the client’s customer experience by making product discovery smarter and more relevant.

+35% Conversion Rate

Customers who interacted with our retail AI agent showed much higher engagement with a 35% increase in add-to-cart and purchase actions.

+20% in Average Order Value

Smarter cross-sell and up-sell recommendations helped users find complementary items. Our client saw a 20% increase in basket size.

Personalized Experience at Scale

The system scaled across 3 geographies and handled 30K+ concurrent sessions without latency issues. Our solutions delivered hyper-personalized shopping experiences.

How We Built an AI Agent That Understands Shoppers in Real Time