The missing infrastructure layer for long-lived AI systems. As AI agents and copilots move into production, teams face a structural problem: context is fragile, temporary, and unsafe to manage ad-hoc. EvoContext turns conversations and events into durable, governed memory that systems can rely on over time.
Initial MVP: Memory infrastructure for Telegram bots and conversational platforms.
LLMs are no longer used for single interactions. They are becoming persistent systems — agents, copilots, and workflows that operate continuously across users, tools, and environments.
But today, there is no reliable system of record for AI context.
Teams stitch together chat logs, vector databases, and prompts — creating brittle systems that forget, hallucinate, or violate privacy constraints at scale.
EvoContext exists to solve this gap: a dedicated memory layer that manages context across time, models, and applications.
This distinction is critical. EvoContext operates at the infrastructure layer, not the application layer.
Instead of replaying entire histories, EvoContext retrieves only the most relevant memories — scored, cited, and time-aware. This makes AI behavior explainable, auditable, and safer to deploy in production environments.
By managing memory across time, models, and applications, EvoContext becomes deeply embedded in AI system architecture. Together, these capabilities form a unified memory substrate that multiple products and agents can share.
Architecture designed for real-time use cases with hybrid semantic and keyword search.
Semantic understanding with keyword precision. Get the most relevant context every time.
Built-in deletion, scoping, and data governance for production environments.
Consolidation, clustering, and decay over time. Memory that adapts automatically.
Works across OpenAI, Anthropic, and open models. Bring your own LLM.
Visibility into memory usage and retrieval quality for debugging and optimization.
These are entry points, not endpoints. Any system that runs AI over time requires memory.
Persistent memory across sessions enables agents that learn preferences, remember decisions, and operate autonomously over time.
Shared context across tickets and channels reduces repetition, improves resolution, and enables safe automation.
Applications maintain continuity — voice, intent, and state — across long-running workflows.
This pipeline stays simple while scaling to millions of interactions. Designed for systems handling millions of AI interactions over time.
Conversations, documents, and events
Extract durable facts and decisions
Return only what matters, with citations
Merge, expire, and summarize memory continuously
We are onboarding a small number of design partners building persistent AI systems. Inbound from partners and investors welcome.