IA se pierde en chats largos — filtro de memoria contextual (recuperación selectiva)

Many mid-sized and large companies building AI assistants and task-specific agents find that language models "lose track" in long chats or multi-agent workflows, causing irrelevant retrieval, context dilution, and hallucinations that increase support costs and reduce trust. This is especially painful for product teams, customer support, and developer tooling in roughly 2 million mid+large enterprises standardizing on retrieval-augmented pipelines. You could build a configurable contextual memory filter — a selective retrieval and memory orchestration layer that ranks and prunes candidate memories by salience, recency, role, and policy before they reach the LLM, with SDKs and connectors to vector DBs, embedding services, and model APIs. The product should expose explainable relevance signals, per-workflow policies, privacy controls, and a low-latency microservice that teams can pilot and integrate into existing RAG stacks, with a realistic go-to-market target of the $12K ACV segment. The timing is favorable: we estimate a $24.0B addressable market (2M mid+large companies × $12K ACV) as RAG, embeddings, and composable AI infra become standard and LLMs are adopted as primary interfaces. Firms are shifting from monolithic chatbots to specialized assistants (coding, planning, support) that need filtered, relevant context instead of indiscriminate memory dumps. To stand out, prioritize measurable precision/recall tradeoffs, explainability (show which memories influenced a response), multi-vector DB orchestration to reduce vendor lock-in, and enterprise controls and SLAs — these are defensible differentiators against medium competition. Be honest about the work required: significant engineering for low-latency retrieval, integration across heterogeneous embedding formats, and strong evaluation frameworks to prove ROI in pilots before scaling to enterprise deals.