How Chatarmin Ditched RAG and Went Memory-Only with Supermemory

Chatarmin, a WhatsApp marketing platform for ecommerce brands, replaced its resource-intensive retrieval-augmented generation pipeline with Supermemory’s persistent memory layer to improve AI-assisted customer conversations. Its previous RAG workflow required query embeddings, vector-store searches, context assembly, and repeated processing on each turn, contributing to response times approaching 40 seconds and high token use. By relying on persistent conversational memory recalled in milliseconds, supplemented by near-real-time web search for changing information, the company reduced average response times to 12 seconds, cut token consumption by 40–50%, and eliminated RAG infrastructure maintenance while retaining personalized conversational context.

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