Kalsnet (KNet) Consulting Group | AI • Cyber • Agentic Systems/Knowledge-Graph-Applications
This Knowledge Graph AI Use Cases Application, built by Randy Singh of Kalsnet (KNet) Consulting Group as a single-file Streamlit tool, models fifteen enterprise scenarios — including cybersecurity threat intelligence, fraud and anti-money-laundering detection, supply chain risk, healthcare intelligence, financial risk, compliance, IT root-cause analysis, defense mission planning, and customer churn — as interactive knowledge graphs of entities and relationships rather than flat tables. For each use case it generates synthetic node/edge data or accepts real CSV uploads, computes graph analytics (centrality, PageRank, shortest-path chains) using NetworkX, renders an interactive Plotly network visualization, and sends a graph summary to a live LLM (Groq or Gemini) that explains findings like attack paths, fraud rings, or root causes in plain language. Notably, the Fraud and AML tabs can pull genuine live data from the OpenSanctions sanctions/PEP screening API and the real-world labeled Elliptic Bitcoin transaction dataset. Results export to PDF, Word, text, and CSV via ReportLab and python-docx. The real-world benefit is turning disconnected records into an explainable, relationship-aware view that reveals hidden risk (shared devices in fraud rings, cascading supplier failures, compliance gaps, attack chains) that row-by-row analysis would miss, while remaining demoable on synthetic data yet credible enough to run against genuine screening and AML datasets for actual investigative work.
Launch App