A developer-focused context and cognitive architecture engine solving LLM context loss across multi-turn sessions and agentic workflows.
LLMs are stateless by default. Multi-turn AI agents suffer from context rot, lost instructions, hallucination, and expensive repetitive prompt token overhead.
AI developers, enterprise software teams, and researchers deploying autonomous agents or long-horizon customer assistants.
Spearheaded the core context-structuring architecture, knowledge graph synthesis, developer ergonomics, and ecosystem distribution strategy.
Persistent memory structuring API endpoints, vector indexing with semantic retrieval, entity relationship clustering, and cognitive inspection dashboard.