Memory-RAG SaaS — Long-Term Context for AI Agents
A memory-focused RAG platform that gives AI agents durable, structured long-term memory across sessions — so chatbots and copilots stop forgetting the things that matter.
What AI productivity SaaS was up against
Most LLM-powered products treat each conversation as a fresh session. Users churn when their assistant keeps re-asking the same setup questions or forgets preferences it had yesterday. Naive RAG over a conversation history is noisy, slow, and expensive. The client needed a memory layer that could distinguish facts from fluff, decay irrelevant memories, and retrieve precisely without ballooning token costs.
What we built
We built a multi-tiered memory layer: short-term working memory (session buffer), episodic memory (salient events with extracted entities and relationships), and semantic memory (distilled facts and preferences). An LLM-powered summariser runs on conversation end; a retrieval scorer blends vector similarity, recency, and frequency; a forgetting policy prunes low-value memories. The whole stack ships as a multi-tenant SaaS with per-user memory isolation, audit tools, and a simple developer SDK.
What shipped
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