Retail · Case study
Retail Personalization & Commerce AI
Recommendation engine and conversational shopping assist that keep catalog, inventory, and offers in sync.
Outcome: 32% higher conversion
The challenge
Generic storefronts under-converted. Merchandisers lacked tools to personalize journeys, and shoppers abandoned when inventory or recommendations felt wrong.
Our solution
We layered OpenAI + LangChain assistants for product discovery on a Next.js storefront, with Redis-backed session context, MongoDB catalog sync, and recommendation logic tied to real stock—so online and campaign experiences stay aligned.
Results
- Conversion up about 32% on personalized journeys
- Fewer abandoned carts from stale recommendations
- Faster merchandising experiments
- Conversational assist reduced support load on product Q&A
Tech stack
Core technologies used on this engagement.
Next.js
TypeScript
Python
OpenAI
LangChain
MongoDB
Redis
AWS
Tailwind CSS
Services involved
Capabilities that powered this delivery.
Web & Mobile Applications
Responsive web and mobile experiences that engage users and convert.
Learn moreAI & Intelligent Solutions
Voice agents, agentic RAG, document intelligence, and automation built for real workflows.
Learn moreCloud & Infrastructure
Secure, scalable cloud setups and reliable infrastructure foundations.
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