Case Study
eCommerce AI Agent (RAG + Agentic AI)
Project Summary
We developed an AI-powered eCommerce Agent that can be seamlessly installed on any Magento 2 website with just a one-line snippet. Once deployed, the agent automatically reads and vectorizes store data (products, CMS pages) into ChromaDB, enabling lightning-fast AI-driven interactions. The agent integrates advanced RAG + Agentic AI pipelines to ensure store-specific accuracy, natural-language-powered search, and personalized recommendations—all delivered with a 10-minute no-code setup.
Project Goal
The goal of this project was to redefine the shopping experience by making it faster, smarter, and more personalized. Instead of navigating traditional eCommerce catalogs, customers can now interact directly with an AI agent that understands intent, retrieves precise information, and recommends the right products at the right time; ultimately driving higher engagement and conversions. Instead of navigating traditional eCommerce catalogs, customers can now interact directly with an AI agent that understands intent, retrieves precise information, and recommends the right products at the right time — ultimately driving higher engagement and conversions.
Core Features
- Instant Plug & Play
- AI-Powered Smart Seach
- Personalized Recommendations
- Secure Magento Authentication
- Confidence Scoring for AI Responses
- Sleek ReactJS UI with seamless animations
- Setup in under 10 minutes
Technology Used
LangChain
OpenRouter
AWS SQS
Major
Challenges
Seamless Integration: No Extensions, No Compatibility Headaches
❗ Challenge
Traditional Magento AI solutions often depend on deploying extensions, which can conflict with site customizations and break with updates, especially across legacy or heavily customized Magento 2 sites. Maintaining stability and ensuring that AI agents work across multiple versions is a major pain point.
✅ Solution
Instead of relying on extensions, our approach leverages the official Magento APIs to ingest all required product, CMS, and order data. The AI agent is then embedded into the storefront using a single-line iframe insert—requiring no code changes and never conflicting with the core Magento install.
This approach ensures:
- Universal compatibility across all Magento 2 sites (current or legacy)
- No risk of breaking store functionality during upgrades
- Simplified, less than 10-minute setup for merchants
- Full decoupling of AI features from Magento codebase, resulting in higher reliability and dramatically easier support compared to extension-based solutions
Data Quality, Inconsistency, and Vectorization
❗ Challenge
Magento stores showed massive variance in data formats, content quality, and schema use—making automatic ingestion and conversion to vectors a source of potential hallucinations and irrelevant or biased AI answers.
✅ Solution
Devised a robust ETL pipeline: automated cleaning, normalization, and validation routines pre-processed data prior to vectorization. Enhanced result accuracy by introducing a confidence scoring mechanism and fallback logic, surfacing uncertain or lower-score results only with clear warnings to users.
Preventing AI Hallucinations, Bias, and Unreliable Results
❗ Challenge
AI models (including top LLMs) risk generating incorrect or biased outputs—and eCommerce stakes are high for mischarging the wrong product, spreading misinformation, or annoying customers with poor recommendations.
✅ Solution
RAG pipelines confined answers to only store-specific, up-to-date, vectorized data (never just model memory). Added transparency with visible confidence percentages and sources, and performed recurring audits on recommendations for fairness, diversity, and factual accuracy.
Real-Time Personalization at Scale
❗ Challenge
Maintaining millisecond response times for search and recommendations, even as user counts and catalog sizes grew, was critical to user experience and a common stumbling block in LLM-driven eCommerce.
✅ Solution
Adopted hybrid retrieval (ChromaDB + Redis + PostgreSQL), with in-memory caches and batched asynchronous queries. This enabled personalization (order history, preferences) to be considered instantly for thousands of concurrent sessions without service lag
Secure Handling of Sensitive eCommerce Data
❗ Challenge
AI-driven interactions required reading and vectorizing all store data, including products, orders, and CMS content. Ensuring compliance (GDPR/CCPA), preventing unauthorized data exposure, and maintaining customer privacy was paramount.
✅ Solution
Implemented end-to-end encryption for all data in transit and at rest within ChromaDB and AWS services. Leveraged Magento APIs for granular access control, ensured no sensitive customer data left the secure backend, and subjected storage and retrieval logic to continuous security audits.
User Trust, Brand Voice, and Consistency
❗ Challenge
AI messages even if accurate had to align with brand tone and not sound robotic or generic. Loss of consistency can undermine trust in the shopping agent.
✅ Solution
Finetuned AI prompts and trained model contexts on brand guidelines and sample content supplied by the merchants, supported by manual override and review by store administrators when needed.
Key Features
AI-Powered Smart Search
Delivers fast, natural language search results that understand complex queries and allow follow-up refinement. Customers find the right product instantly without manual browsing.
Personalized Recommendations
Offers context-aware, customer-specific product suggestions using RAG and historical interactions to increase add-to-cart and checkout conversions.
Chat With AI
Conversational interface lets users ask about products, orders, invoices, or account updates as if they were chatting with a human agent.
Secure Auth & Privacy
All AI interactions are handled securely using Magento APIs to ensure customer information and transactions stay private.
Personalized Dashboard
AI agent gives customers a one-glance view of their critical account details, order history, and recommendations reducing friction across their shopping journey.
Confidence Scoring System
Displays reliability percentages alongside every AI response, recommendation, and search result so customers know exactly how much they can trust each answer.
The Outcome
The pilot project was deployed on 10+ Magento store with a blended customer based or around 85,000 and weekly orders averaging around 3500 orders.
of customers started using the AI Agent for product discovery & information
higher conversion rates compared to standard Magento stores.
customers rated the AI Agent as more efficient than traditional stores (customer survey).
of queries were resolved end-to-end without human support.
reduction in time from average spent per customer on support Minutes/hours came down to seconds.
