GraphRAG & AI Orchestration
We connect your enterprise data into a knowledge graph so AI answers are grounded, explainable and auditable, then put that graph to work with agents that act on it.
Neo4j Graph Implementations
We specialize in designing and deploying Neo4j Knowledge Graphs that map complex relationships across enterprise data. By implementing Graph Data Science (GDS) algorithms, we uncover patterns in fraud detection, supply chain logistics, and customer path analysis. As a Neo4j Certified Professional, we design graphs that hold up at production scale.
Graph Data Pipeline & Kafka
High-performance AI requires real-time data orchestration. We implement low-latency event streaming using AWS MSK and Apache Kafka, ensuring your knowledge graph remains synchronized with production environments. By leveraging Change Data Capture (CDC) and custom Sink Connectors, we can replicate complex relational mutations into a high-fidelity graph layer for instantaneous retrieval.
GraphRAG & Agentic Orchestration
We build Retrieval-Augmented Generation (RAG) systems that utilize the structural connections of Knowledge Graphs to ground Large Language Models. Using frameworks like LangChain and LlamaIndex, we develop custom indexing strategies that combine Vector Search with Cypher-based pathfinding. This dramatically reduces hallucinations, makes every answer traceable to its source, and provides a verifiable basis for Agentic AI to execute complex, multi-step business logic via Model Context Protocol (MCP) servers.
Ontology Surfacing
Once the graph exists, we derive its ontology: the concepts and relationships your data actually contains. That map shows where coverage is thin, where sources disagree, and gives multi-agent systems a shared vocabulary to reason with.
In Practice
Product traceability for a multi-line manufacturer
A Neo4j knowledge graph unifying SAP batch genealogy, materials and quality data: 80M+ genealogy relationships and 250M+ sampling records, with sub-second traceability from a serialized unit back to its source material lots. Graph Data Science (FastRP, KNN, Louvain) turns it into an active quality-intelligence platform for root-cause analysis and recall containment.
AI engineering suite for an oil & gas engineering firm
21 AI applications that automate P&ID analysis, drawing validation and QA/QC, built on Gemini and Claude with a strict cite-your-source design so every answer is auditable. See Schematic Intelligence →
More examples in our portfolio →
How We Engage
- Assessment — map your data, use cases and constraints; identify where a graph pays off first.
- Pilot — a focused graph and GraphRAG prototype on real data, with measurable success criteria.
- Production — hardened pipelines, agents and deployment on your cloud, with knowledge transfer to your team.