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

Los asistentes de IA se confunden en conversaciones largas porque todo llega al modelo. Solución: un módulo ligero que decide qué memoria recuperar según el modo (código vs estrategia) para enviar solo lo relevante y evitar alucinaciones.

87Score

Target Audience

Equipos de desarrollo y producto de startups AI-first y empresas tecnológicas (5-200 empleados) que usan LLMs en flujos críticos (asistentes, copilotos, docs), además de equipos de soporte técnico que necesitan respuestas consistentes.

Market Size

$24.0B = 2M mid+large companie...

Competition

medium

Key Pain Points

Low technical barrier -- core techniques (filters + RAG) are easy to copy, reducing defensibility without data/network effects.
Customer inertia & integration cost -- teams may prefer to bolt simple heuristics into current RAG stacks rather than buy another service.
Privacy/regulatory constraints -- strict data policies may limit ability to centralize memory or require on-prem options, increasing implementation complexity.

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OVERALL

8.7Great

Market Validation

Demand

~2K/mo*

Competition

medium

Growth

30-40% sector CAGR driven by enterprise AI adoption*

Market Size

$24.0B

Market Opportunity

IA se pierde en chats largos — filtro de memoria contextual (recuperación selectiva) targets a $24.0B = 2M mid+large companies × $12K ACV (enterprise AI assistant orchestration & memory services) total addressable market with medium saturation and a year-over-year growth rate of 30-40% sector CAGR driven by enterprise AI adoption.

Key trends driving demand: RAG & embeddings -- companies standardize on retrieval-augmented pipelines as LLMs become primary interfaces.; Specialized assistants -- shift from monolithic chatbots to task-specific agents (coding, planning, support) that need filtered context.; Composable AI infra -- vector DBs, embedding services, and model APIs enable rapid integration of memory layers.; Privacy & data-localization -- teams want selective retrieval that avoids leaking PII or irrelevant internal context..