semantica/docs/use-cases.md

title, description, icon

title	description	icon
Use Cases	Real-world applications of Semantica across domains, with linked cookbook notebooks for each.	briefcase

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

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`, `context`

**Notebooks:**
- [Drug Discovery Pipeline](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/biomedical/01_Drug_Discovery_Pipeline.ipynb): Intermediate
- [Genomic Variant Analysis](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb): Intermediate

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`, `llms`

**Notebooks:**
- [GraphRAG Complete](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb): Advanced
- [RAG vs. GraphRAG Comparison](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/advanced_rag/02_RAG_vs_GraphRAG_Comparison.ipynb): Advanced

Unify financial data from APIs, MCP servers, and real-time streams into a single queryable knowledge graph: with conflict detection when sources disagree.

**Key modules:** `ingest` (API, MCP, stream), `normalize`, `kg`, `conflicts`, `provenance`

**Notebooks:**
- [Financial Data Integration (MCP)](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/finance/01_Financial_Data_Integration_MCP.ipynb): Intermediate

Detect complex fraud rings using temporal graphs and pattern detection over transaction, device, and user data. Temporal edges let you query: "what connections existed during this window?"

**Key modules:** `kg` (temporal), `conflicts`, `reasoning`, `visualization`

**Notebooks:**
- [Fraud Detection](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/finance/02_Fraud_Detection.ipynb): Advanced

Map transaction flows, analyze DeFi protocols, and detect illicit activity. Graph algorithms (centrality, community detection) surface high-risk actors that linear transaction analysis misses.

**Key modules:** `kg`, `reasoning`, `visualization`

**Notebooks:**
- [DeFi Protocol Intelligence](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/blockchain/01_DeFi_Protocol_Intelligence.ipynb): Intermediate
- [Transaction Network Analysis](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb): Intermediate

Ingest threat feeds (CVE databases, security RSS), detect anomalies in streaming data, and build threat intelligence knowledge graphs for proactive defense.

**Key modules:** `ingest` (stream, feed), `kg` (temporal), `context`, `reasoning`, `export`

**Notebooks:**
- [Real-Time Anomaly Detection](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/cybersecurity/01_Real_Time_Anomaly_Detection.ipynb): Advanced
- [Threat Intelligence Hybrid RAG](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/cybersecurity/02_Threat_Intelligence_Hybrid_RAG.ipynb): Advanced

Build knowledge graphs from police reports, court records, and OSINT feeds to identify key players, communities, and suspicious patterns. Network centrality surfaces actors text search alone would miss.

**Key modules:** `ingest`, `semantic_extract`, `kg`, `visualization` (community detection)

**Notebooks:**
- [Criminal Network Analysis](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/intelligence/01_Criminal_Network_Analysis.ipynb): Intermediate

Process multiple intelligence sources in parallel with an orchestrator-worker pipeline. Multi-source conflict detection flags disagreements rather than silently discarding minority reports.

**Key modules:** `pipeline`, `ingest`, `conflicts`, `provenance`, `export`

**Notebooks:**
- [Intelligence Analysis Orchestrator-Worker](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/intelligence/02_Intelligence_Analysis_Orchestrator_Worker.ipynb): Intermediate

Map suppliers, logistics routes, inventory levels, and delivery relationships to identify bottlenecks and optimize global supply chains. Graph path-finding reveals indirect dependencies spreadsheets miss.

**Key modules:** `ingest`, `kg`, `reasoning`, `visualization`, `export` (Parquet for analytics)

**Notebooks:**
- [Supply Chain Data Integration](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/supply_chain/01_Supply_Chain_Data_Integration.ipynb): Intermediate

Connect sensor data, weather forecasts, and maintenance logs to predict equipment failures and optimize grid operations. Temporal graphs track asset states over time and correlate maintenance events with performance degradation.

**Key modules:** `ingest` (stream, API), `kg` (temporal), `reasoning`, `visualization`

**Notebooks:**
- [Energy Market Analysis](https://github.com/semantica-agi/semantica/blob/main/cookbook/use_cases/renewable_energy/01_Energy_Market_Analysis.ipynb): Intermediate

Compliance Footprint by Domain

Regulatory requirements: HIPAA, FDA 21 CFR Part 11

Semantica capability	Compliance role
W3C PROV-O provenance	Full lineage from raw data to inference: required for FDA audit trails
SHA-256 checksums	Tamper detection on every snapshot: supports electronic record integrity
Decision tracking	Every AI-assisted recommendation is recorded with causal chain and confidence
Temporal graphs	Point-in-time queries for retrospective safety analysis
SHACL validation	Schema enforcement before data enters the knowledge graph

Regulatory requirements: SOX, MiFID II, GDPR, Basel III

Semantica capability	Compliance role
Decision audit trail	Full record of model decisions with reasoning: required for model risk management
Conflict detection	Flags when two sources disagree on a valuation or risk figure
Version control	SHA-256 snapshot history: supports point-in-time reconstruction for audits
Provenance export	RDF with PROV-O inline: submittable to regulatory bodies as structured evidence

Operational requirements: Air-gap capability, chain-of-custody, information provenance

Semantica capability	Operational role
Local LLM support	HuggingFaceLLM and Ollama via LiteLLM: fully air-gapped deployments
Provenance chains	Every intelligence claim traceable to source document and extraction event
Conflict resolution	Multiple-source disagreement resolved with auditable strategy
Temporal intelligence	Historical queries over evolving intelligence graphs

Requirements: Evidence integrity, chain of custody, regulatory change tracking

Semantica capability	Legal role
Source attribution	Every extracted fact links to document, page, and section
PROV-O export	Structured provenance acceptable as supporting evidence
Change management	Version-controlled knowledge bases with diff and rollback
Reasoning paths	Explainable inference chains for contested conclusions

Difficulty Reference

Level	What it means	Typical time
Beginner	Basic Semantica knowledge only, no domain expertise needed	30–60 min
Intermediate	Some domain knowledge helpful, uses 2–4 Semantica modules	1–2 hours
Advanced	Domain expertise expected, uses temporal graphs, multi-source pipelines, or reasoning	2–3 hours

Full notebook catalog organized by topic and difficulty. Every module with code examples. Complete technical documentation. Have a use case to add? [Open a PR](https://github.com/semantica-agi/semantica) or start a discussion on GitHub.