Semantica is purpose-built for environments where AI outputs must be explainable, auditable, and traceable. Every use case below includes linked Jupyter notebooks you can run today.

Browse by Sector

Biomedical Knowledge Graphs

Connect genes, proteins, drugs, and diseases from scientific literature to accelerate drug discovery and understand disease mechanisms.Key modules: ingest (PubMed RSS), semantic_extract, kg, deduplication, contextNotebooks:

GraphRAG for Research

Ground LLM answers in structured scientific literature with hybrid retrieval, logical inference, and source attribution on every claim.Key modules: context, vector_store, kg, reasoning, llmsNotebooks:

Compliance Footprint by Domain

Regulatory requirements: HIPAA, FDA 21 CFR Part 11
Semantica capabilityCompliance role
W3C PROV-O provenanceFull lineage from raw data to inference: required for FDA audit trails
SHA-256 checksumsTamper detection on every snapshot: supports electronic record integrity
Decision trackingEvery AI-assisted recommendation is recorded with causal chain and confidence
Temporal graphsPoint-in-time queries for retrospective safety analysis
SHACL validationSchema enforcement before data enters the knowledge graph
Regulatory requirements: SOX, MiFID II, GDPR, Basel III
Semantica capabilityCompliance role
Decision audit trailFull record of model decisions with reasoning: required for model risk management
Conflict detectionFlags when two sources disagree on a valuation or risk figure
Version controlSHA-256 snapshot history: supports point-in-time reconstruction for audits
Provenance exportRDF with PROV-O inline: submittable to regulatory bodies as structured evidence
Operational requirements: Air-gap capability, chain-of-custody, information provenance
Semantica capabilityOperational role
Local LLM supportHuggingFaceLLM and Ollama via LiteLLM: fully air-gapped deployments
Provenance chainsEvery intelligence claim traceable to source document and extraction event
Conflict resolutionMultiple-source disagreement resolved with auditable strategy
Temporal intelligenceHistorical queries over evolving intelligence graphs

Difficulty Reference

LevelWhat it meansTypical time
BeginnerBasic Semantica knowledge only, no domain expertise needed30–60 min
IntermediateSome domain knowledge helpful, uses 2–4 Semantica modules1–2 hours
AdvancedDomain expertise expected, uses temporal graphs, multi-source pipelines, or reasoning2–3 hours

Cookbook

Full notebook catalog organized by topic and difficulty.

Modules Guide

Every module with code examples.

API Reference

Complete technical documentation.
Have a use case to add? Open a PR or start a discussion on GitHub.