Knowledge graphs are the missing layer for agentic AI.
An AI agent without structured data is a self driving car with no map: confident, fast, and one wrong turn from a hallucination. We build the knowledge graph and GraphRAG layer that makes agents accurate, explainable, and auditable in production.
No obligation. We’ll show you exactly where your stack breaks on multi hop questions.
0
Retrieval phases
Anchor · Expand · Cite
0
IP ownership
No lock in, no revenue share
0
Deployment
Your cloud boundary, your control
Why do AI agents without structured data fail?
The Answer
Because vector search retrieves what looks similar, not what is factually connected: so agents break on multi-hop reasoning and fill the gap with a confident guess.
Ask a pure vector agent, “which of this patient’s medications interact with the drug we’re about to prescribe?” and it retrieves passages that read like the question: not the specific chain of facts that answers it. The relationships it needs to follow simply aren’t stored anywhere it can traverse. The result is the failure mode every CTO has seen in a demo: a fluent answer that is subtly, dangerously wrong.
How does a knowledge graph fix it?
The Answer
A knowledge graph stores your domain as entities and typed relationships, so a GraphRAG agent retrieves by traversing real connections and returns the exact path of evidence behind every answer.
Structure turns retrieval from a similarity guess into a surgical traversal: entities, relationships, and constraints the agent can walk. Three properties fall out of that: retrieval is precise, every answer is explainable, and the graph improves as your data does.
Step by step
How a knowledge graph powers an AI agent
The path from raw domain data to a graph grounded agent that cites its work.
- 1
Model the domain as a knowledge graph
A senior architect maps your entities, relationships, and constraints into an ontology: the structured ground truth your agents reason against. Versioned, reviewed, and owned by your team.
- 2
Ground retrieval with GraphRAG
Instead of a similarity guess over text chunks, the agent traverses the graph: following typed relationships to retrieve exactly the connected facts a question needs, with the path it took attached.
- 3
Let the agent plan, act, and cite
The agent reasons over graph grounded context, executes its task, and returns an answer with a traceable chain of evidence: every claim tied back to a node your compliance team can audit.
Where it lands
Graph grounded agents, in regulated production.
Healthcare
Clinical agents that cite the chart, not a guess
Patient 360 over a HIPAA compliant graph: an agent that answers a clinician's question by traversing the record, prescriptions, and guidelines, and shows exactly where each fact came from.
See how knowledge graphs power healthcare AIFintech
KYC/AML agents that survive an audit
An agent that resolves entities across accounts and counterparties, follows relationships to surface hidden risk, and hands your regulator a defensible, explainable trail for every decision.
See how knowledge graphs transform fintech AIThe Guaranteed 30 Day Production Pilot
A graph grounded AI agent in production in 30 days: or you don’t pay for the pilot.
Not a slide deck and not a sandbox demo: a working Neo4j knowledge graph and GraphRAG agent deployed inside your cloud boundary, mapped to your compliance controls, and handed over with the schema, the eval harness, and the runbook.
Architecture and success criteria signed off in week one; a working graph grounded agent running in your environment inside 30 days.
Fully done for you. Our senior squad owns the ontology, the graph build, the GraphRAG retriever, and the eval harness: your team reviews and signs off.
Fixed scope, fixed price, and a measurable accuracy target agreed in writing before we start. Miss the target and you don't pay for the pilot.
All lanes · Grounded AI
Confident isn't the same as correct.
An LLM alone is confident even when it's wrong. Every answer our agents give traces back through the graph to a real source first.
- Up to 80% fewer hallucinations, target agreed in writing
- A citation on every answer, not just the confident ones
- Miss the agreed reduction target and the pilot is free
Your data sources
Documents, databases, systems of record
Knowledge graph
Entities, relationships, and a citation on every fact
AI agent / RAG
Answers with a traceable source, not a guess
Enterprise · Why graph
The deeper the relationship, the wider the gap.
As query depth increases, relational joins compound and slow down. Graph traversal cost stays close to flat, because it follows relationships instead of re-joining tables.
12+ joins, one query
Every hop re-executes the join.
A few hops, one traversal
Same cost, no matter the depth.
Illustrative, not a measured benchmark on your data. Reflects the widely documented shape of graph vs relational performance as relationship depth increases; run your own workload to see your numbers.
People also ask about knowledge graphs for AI agents.
Why do AI agents need a knowledge graph?+
Agents that rely only on vector search retrieve text that looks similar to the question, not text that is factually connected to it. That breaks on multi hop reasoning: 'which of this customer's counterparties are within two hops of a sanctioned entity?' A knowledge graph stores those relationships explicitly, so the agent can traverse them and return an accurate, explainable answer.
How is GraphRAG different from ordinary RAG?+
Ordinary RAG embeds documents into vectors and retrieves the nearest chunks: it has no model of how facts relate. GraphRAG retrieves by walking a knowledge graph, following typed relationships, so it fetches exactly the connected facts a question needs and returns the path it took. That means surgical retrieval and full explainability instead of a similarity guess.
Do you use Neo4j, Amazon Neptune, or something else?+
We choose the platform for your constraints. Neo4j for rich Cypher traversal and developer ergonomics; Amazon Neptune when you're standardised on AWS and want a managed service; RDF/SPARQL stores when you need formal ontologies and reasoning. Everything is deployed inside your own cloud boundary: your data never leaves.
How long until we have something in production?+
A fixed scope production pilot runs 30 days: week one to align on the ontology and success criteria, then delivery in short sprints with a graph grounded agent live in your environment by the end. You keep the schema, the retriever, and the documentation.
Will our team be locked in?+
No. You own the ontology, the ETL, the query library, and the eval harness from day one. There's no per seat licensing and no revenue share. When we exit, your team can operate and extend the system without us.
Enterprise · Confidential Briefing
Request a Confidential Briefing
A senior architect responds within a few hours, under mutual NDA if you prefer. You keep the $50K+ findings report either way.
- SOC 2 / HIPAA / GDPR / PCI-DSS ready architecture
- Deployed in your VPC, on your cloud
- Findings report yours to keep, no obligation
Senior architect replies within a few hours. Findings report is yours to keep, no obligation. Prefer to talk now? Call +1 (469) 947-4244.
Which lane fits you?