A modern multimodal knowledge graph with type-specific metadata across biomedical domains.

Graph AI for Quantitative Evaluation of Compatibility in Traditional Chinese Medicine (TCM)

Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems

An explainable AI system that combines Graph Intelligence, Vector Search, and Retrieval-Augmented Generation (RAG) to deliver grounded answers and transparent reasoning paths. Includes a FastAPI backend, Streamlit UI, FAISS vector index, and an in-memory knowledge graph for hybrid retrieval and recommendations.

AI-native Agentic AI platform for customs & HTS classification, tariff analysis, trade compliance and multi-tier supply chain risk intelligence.

From MVP to business value: explainable AML AI ready for real-world deployment.

Explore AI writing systems with agents, graphs, adapters, RAG, and multi-model generation workflow.

https://himanshuvnm.github.io/gift-kastl/ 2025 SciFM Conference Research Poster on the graph foundation model on the Discrete Fracture Networks dataset. We introduced a novel neural net layer to achieve high-end approximation based on the Kolmogorov-Arnold Superposition Theorem which now is also used in constructing KAN.

🧠 Graph-based RAG system that transforms unstructured data into knowledge graphs for deeper reasoning and contextual AI responses. Combines LLMs, graph traversal & semantic search to enable multi-hop question answering and structured insights.

Graph AI platform connecting equipment, processes, materials, and failure modes — enabling complex relationship queries and causal analysis across manufacturing operations

Compare LLM text embeddings with structure-aware Graph AI (GNN link prediction) on any dataset with nodes, text, and edges.