Most retrieval layers store embeddings without a structured trail, making consequential AI decisions hard to defend. Semantica inserts a deterministic Context Graph and Decision Intelligence layer beneath your LLM and vector store so every fact, relationship, and decision is queryable, time-travelable, and linked to provenance — enabling regulator-ready audit trails without requiring an LLM to build the graph.
What Sets It Apart
- Polyglot, swappable storage: native RDF (Oxigraph, Blazegraph, Jena, RDF4J) and labeled property graph backends (Neo4j, FalkorDB, Apache AGE, AWS Neptune), plus vector store integrations — so you can change storage without rewriting code.
- Decisions as first-class objects: record_decision() creates queryable, causally-linked decision nodes exportable as W3C PROV-O, making "why" and "what happened next" machine-readable and auditable.
- Deterministic, explainable reasoning: forward chaining, Rete networks, Datalog and SPARQL with explainable inference paths instead of opaque LLM outputs.
- Enterprise connectors & tooling: Databricks and Snowflake ingestors (preserve lineage), a Knowledge Explorer workbench for visual audits, and an MCP/REST surface for agent integration.
Who It's For & Tradeoffs
Great fit if you need auditable, self-hosted AI systems in regulated domains (finance, healthcare, legal, government) or if you want graph-native context and conflict-aware KG construction for multi-agent teams. Look elsewhere if you only need lightweight vector-RAG retrieval: Semantica adds operational complexity and governance primitives best justified for high-stakes or compliance-driven deployments.
Where It Fits
Acts as the auditable, semantic memory and reasoning layer beneath agents and LLMs — complements existing LLMs/vector stores rather than replacing them.
