Why Knowledge Graphs Matter for AI
Large language models are probabilistic — they hallucinate facts, confuse entities, and misattribute claims. The antidote is a knowledge graph: a structured, machine-readable representation of facts, relationships, and entities specific to your brand and domain.
When your knowledge graph is properly integrated, LLMs stop guessing and start citing your verified truth.
What We Do
Entity Relationship Mapping
We identify every key entity in your business — products, people, locations, concepts, events — and build an explicit graph of how they relate to each other and to the broader web.
JSON-LD Knowledge Base
We implement your knowledge graph as a linked set of JSON-LD documents, cross-referenced with schema.org vocabularies and Wikidata entities to maximize LLM legibility.
Proprietary Fact Grounding
We create machine-readable fact sheets for your most commercially important claims — specifications, prices, certifications, provenance, and differentiators — so LLMs can cite them with confidence.
Knowledge Graph API
For enterprise clients, we expose your knowledge graph as a queryable API endpoint, enabling real-time grounding for RAG (Retrieval Augmented Generation) implementations.
Our Focus
- Data Grounding: Creating reliable facts that LLMs can use to verify information.
- Relational Integrity: Building clear, machine-readable links between your entities.
- RAG Readiness: Structuring your proprietary knowledge to enhance internal and external AI responses.