What if causality were computable?
Everything connects.
Nothing makes sense.
The New Layer for Reasoning.
HContextAI transforms fragmented information into navigable context graphs — enabling systems and people to think with clarity at scale.
In an age of fragmented communication and diverging perspectives, understanding has become a scarce resource. Abundant information often conceals the bigger picture, while the increasing complexity of our technical and societal systems intensifies friction and noise.
Human Computer Context Artificial Intelligence Interaction (HContextAI) is the substrate designed to reverse this entropy. We bridge perspectives and synthesize isolated insights into a unified, addressable context graph. By restoring the connective tissue of information, we provide a trusted source of truth that empowers both people and systems to see clearly again.
Ingest
Connect document sources. HContextAI parses, chunks, and extracts typed entities and relationships while preserving provenance—document, chunk, and source text.
Structure
Relationships are extracted and grounded in source text. Every node is addressable; every edge carries predicate type, confidence, and provenance—including evidence quotes and source documents.
Query
Ask questions in natural language. HContextAI retrieves relevant subgraphs, synthesizes answers from evidence, and returns structured responses with citations—traceable to source documents, not guesses.
HContextAI builds on research in structured knowledge representation and graph-based reasoning at scale.
The initial model, contextGNN0.5, defines a baseline architecture for context-aware graph inference.
- Context Architecture and Graph Semanticsforthcoming
- Relational Reasoning in Large Language Modelsforthcoming
- Addressability as a First-Class Property of Informationforthcoming
CONTACT
INVITATION ONLY.
This is not an open platform. We are selectively onboarding institutions and independent thinkers working with high-density information environments.
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