Semantica

MIT-licensed Python framework providing the semantic intelligence layer that AI applications are missing — bridging raw text and trustworthy, explainable AI.

Problem: Modern RAG pipelines and agents treat knowledge as bags of text chunks with no understanding of entities, relationships, or rules. In high-stakes domains (healthcare, finance, legal, cybersecurity) this produces black-box answers that cannot be explained, audited, or trusted.

What Semantica provides:
• Universal ingestion — 50+ formats (PDF, DOCX, HTML, CSV, databases, APIs)
• Semantic extraction — NER, relationship extraction, event detection
• Knowledge graph construction — conflict detection, deduplication, entity resolution
• Automatic ontology generation — 6-stage LLM pipeline producing OWL ontologies
• GraphRAG — hybrid vector + graph retrieval for 30%+ accuracy gains over plain RAG
• Decision tracking — precedent search, causal analysis, policy compliance
• Provenance — W3C PROV-O compliant lineage from source document to AI response
• Change management — versioned knowledge graphs with SHA-256 integrity verification

Not a LangChain wrapper. The semantic layer that sits beneath your agents and integrates with LangChain, LlamaIndex, AutoGen, and CrewAI.

Traction: 29 production-ready modules, 50+ tutorial notebooks, 14 domain cookbooks (biomedical, finance, cybersecurity, supply chain, and more). MIT-licensed. Self-funded for over a year.