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