semantica-agi/semantica
Graph-Native Infrastructure for Context and Accountable AI Systems
What it solves
Traditional AI agents often operate as "black boxes," relying on flat vector embeddings that lack semantic meaning, auditability, and causal reasoning. This makes it difficult for agents in regulated industries (like finance or healthcare) to explain why a specific decision was made, leading to compliance and risk management challenges.
How it works
Semantica acts as a deterministic infrastructure layer that sits beneath LLMs and vector stores. It ingests multi-source data to construct a structured Context Graph (Knowledge Graph) using both RDF and Labeled Property Graph standards. It uses deterministic reasoning engines (like Datalog and SPARQL) and W3C PROV-O provenance to ensure every fact and decision is traceable, verifiable, and explainable without relying on the LLM for the reasoning itself.
Who it’s for
- AI/ML platform teams building agents that require structured, queryable context rather than just vector indices.
- Compliance and risk teams in regulated sectors (finance, legal, government) who need auditable decision trails.
- Platform and infrastructure engineers seeking self-hostable, vendor-neutral graph and reasoning stacks.
- Data and knowledge engineers needing to build complex knowledge graphs from messy, multi-source data with automated conflict detection.
Highlights
- Decision Intelligence: Treats every AI decision as a first-class, queryable graph node with causal links.
- Graph-Native Context: Uses graph traversal to find connections that simple embedding similarity might miss.
- Full Auditability: Provides W3C PROV-O lineage for every fact and decision, exportable for regulators.
- Polyglot Storage: Supports multiple backends including Neo4j, AWS Neptune, and various RDF triple stores.
- Deterministic Reasoning: Includes forward chaining, Rete networks, and Datalog for explainable logic.