Build semantic layers that connect your data in meaningful ways. Power search, recommendations, and AI reasoning with graph intelligence.
Graphs that make your data relational, searchable, and ready for AI reasoning.
Connect siloed databases, documents, and APIs into a unified graph. Answer questions that no single system could answer alone.
Augment LLM retrieval with graph traversal. Get answers that follow chains of reasoning across entities and relationships — not just keyword matches.
Model your domain with formal ontologies that capture business rules, taxonomies, and relationships — the backbone of intelligent data integration.
Graph-powered recommendations that surface unexpected connections — products, experts, documents, or opportunities your team would otherwise miss.
Work with your subject-matter experts to design the ontology — entity types, relationships, properties, and constraints.
ETL pipelines that extract entities from databases, documents, and APIs, then materialize them as graph nodes and edges in Neo4j or AWS Neptune.
Vector embeddings on graph nodes for hybrid semantic + structural search. Combine full-text, vector, and Cypher queries in a single API.
Wire the graph into your LLM stack as a retrieval source — GraphRAG, tool-calling agents, or direct Cypher generation from natural language.
We've been building knowledge graphs since before the term was mainstream. Our team published research on ontology engineering and semantic web technologies.
Deep expertise in Neo4j, AWS Neptune, and graph databases. We pick the right engine for your scale and query patterns.
We combine knowledge graphs with LLMs in ways most teams haven't explored yet. GraphRAG, graph-guided agents, and ontology-driven prompt engineering.
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or email directly: fernandrez@iseeci.com