Home / Companies / Aerospike / Blog / Post Details
Content Deep Dive

How Myntra built a GenAI shopping assistant that remembers, recommends, and responds in milliseconds

Blog post from Aerospike

Post Details
Company
Date Published
Author
Steve Tuohy
Word Count
1,615
Company Posts That Month
9
Language
English
Hacker News Points
-
Post removed?
No
Summary

Myntra, one of India's largest fashion e-commerce platforms, has integrated Aerospike's real-time GenAI architecture to enhance its GenAI-powered shopping assistant, Maya, facilitating personalized and seamless shopping experiences. Maya allows users to ask questions and receive tailored recommendations, maintaining conversation continuity across sessions and devices. The integration of Aerospike enables Myntra to efficiently handle both durable storage and sub-millisecond data retrieval, crucial for processing millions of clickstream events and delivering real-time personalization. Before adopting Aerospike, Myntra faced challenges with separate systems for feature storage, state management, and caching, which were resolved by unifying these functions under a single Aerospike-backed cluster, enhancing both performance and operational simplicity. This architecture supports the high demands of peak sales events, such as the Big Fashion Festival, enabling Myntra to manage half a million concurrent users and over 20,000 orders per minute while maintaining stable latency. As Myntra continues to expand its GenAI capabilities across the customer journey, the Aerospike infrastructure provides a scalable foundation to support both current and future AI-powered retail innovations.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
Real-time 10 6,457 1,307 242 +28%
RAG 2 1,806 326 91 +5%
AI Agents 1 4,545 963 231 +27%
LLM 1 6,078 960 218 +18%
Vector Search 1 2,370 415 145 +7%
Use This Data

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.