Overview

Semantica is a graph-native infrastructure designed to bring context, accountability, and deterministic reasoning to AI systems. It transforms fragmented enterprise data into a structured Context Graph and knowledge graph, governed by ontologies and standards like OWL and SHACL. This approach ensures explainable, traceable, and trustworthy AI, particularly vital in high-stakes and regulated domains. Unlike embedding-based systems, Semantica focuses on structured meaning and relationships, providing a robust semantic layer beneath LLMs, vector stores, and agent frameworks.

Key Features

• Context Graphs & Decision Intelligence: Creates a queryable graph of all AI knowledge, decisions, and reasoning. Every decision is a first-class, auditable object with traceable provenance and causal links.
• AI Governance & Ontology Management: Implements SHACL constraints, conflict detection, compliance rules, and supports OWL and SKOS vocabularies, offering a visual editor for ontology management.
• Full Auditability & Provenance: Generates W3C PROV-O compliant provenance for every fact, with audit trails exportable in JSON, CSV, or RDF formats.
• Deterministic Reasoning Engine: Powers forward chaining, Rete networks, Datalog, and SPARQL for fully explainable inference paths, independent of LLMs.
• Knowledge Pipeline: Features multi-source ingestion, entity-aware chunking, extraction (NER, relations, events), and semantic deduplication with provenance-preserving merges.
• Enterprise Data Platform Integration: Offers native connectors for Databricks (Unity Catalog), Snowflake, and SAP OData, allowing existing data to become graph nodes without costly exports.
• Polyglot Graph Storage: Supports interchangeable RDF (Oxigraph, Blazegraph, Jena, RDF4J) and Labeled Property Graph (Neo4j, FalkorDB, AGE, Neptune) backends, along with vector stores.
• Interactive Visualization: Provides a workbench for exploring graphs, ontologies, and timelines.
• Seamless Integrations: Includes native support for Agno, CrewAI, and LangChain, a comprehensive CLI, REST API, and MCP server.

Typical Use Cases

• AI/ML Platform Teams: Building agents that require structured, queryable context for consequential decisions, moving beyond simple vector indexing.
• Data Platform Teams: Transforming data within Databricks Unity Catalog or Snowflake into governed, lineage-tracked knowledge graphs without relying on third-party SaaS.
• Compliance, Risk & Audit Teams: Obtaining straight answers to "why did the AI do that?" in a regulator-accepted format.
• Regulated Enterprises: Deploying explainable and traceable AI solutions in finance, healthcare, legal, government, and defense sectors without data sovereignty concerns.
• Platform & Infrastructure Engineers: Implementing self-hosted, swappable KG, reasoning, and provenance stacks, avoiding vendor lock-in.
• Data & Knowledge Engineers: Constructing robust knowledge graphs from complex, multi-source data, managing conflicting facts and duplicates effectively.