Knowledge Graph Consultants
Agentic Giants builds enterprise knowledge graphs on Neo4j, Amazon Neptune, and RDF the connected-data layer regulated organizations need before AI, personalization, or auditable reporting can work. Headquartered in Aubrey, Texas.
What do knowledge graph consultants do?
The Answer
Knowledge graph consultants model your business as typed entities and relationships (customers, contracts, products, transactions) in a graph database, then connect it to the systems and AI applications that need it. You hire one when questions your business asks require joining facts across systems that don't share identifiers, or when regulators need lineage on every figure. The outcome is one queryable source of truth with provenance built in.
What an engagement covers
- Ontology and schema design
- Source-system mapping and ingestion pipelines
- Entity resolution (one customer, one node)
- Graph database architecture and deployment (Neo4j, Neptune, RDF)
- Query and API layer
- Connection to AI / RAG / agentic applications
- Documentation, handover, and team training
Production track record
40% faster data retrieval for an enterprise client
One context layer across tools that previously did not share identifiers, measured against the pre-WorkPods baseline. Four of our team hold Neo4j GraphAcademy certifications, and we deploy on Neo4j, Amazon Neptune, or RDF depending on what your stack and compliance posture call for.
Read the case study →Governance and handover
A knowledge graph your team cannot maintain is a liability with a nice diagram. Every engagement is built around knowledge transfer from the first week, so your internal team owns the model and can extend it without us.
Knowledge transfer from week one
Pairing sessions with your engineers through the build, not a training day bolted on at the end. Your team writes production Cypher before we leave.
Documentation and runbooks
The ontology, the ingestion pipelines, and the operational runbooks written down, so the model survives staff turnover on your side and ours.
Internal capability, not dependency
The goal is that you can extend the graph to a new domain without calling us. Stakeholders who need to maintain the model are in the room while it is designed.
Full IP handover
Built in your repo, on your infrastructure. You own the ontology, the pipelines, and the code. No lock in, no black boxes.
Built in your repository, on your infrastructure. You own the ontology, the pipelines, and the code at the end of the engagement. No lock in, no black boxes.
When you need one, and when you don’t
You need a knowledge graph when your top questions are multi-hop (“which platinum customers raised P1 tickets about the deprecated feature?”) or when auditability is mandatory. You don’t need one yet if your data is a homogeneous document pile and similarity search answers your questions: we’ll tell you that in the discovery stage, in writing.
Frequently asked
Neo4j vs Amazon Neptune, which should we use?
Neo4j is stronger for developer experience, Cypher query ergonomics, and the tooling around graph data science. Neptune is stronger when the rest of your stack lives in AWS and you want a managed service with tight IAM integration. We deploy both and pick during the discovery stage based on your compliance posture, existing cloud commitments, and query patterns, not vendor loyalty.
How long does a first knowledge graph take?
Discovery and ontology design run one to two weeks. A first production graph with three to five source systems ingested and a working query API typically ships in six to eight weeks. Entity resolution across messy source data is the biggest variable: we scope it explicitly in discovery so nothing surprises you.
Do we need to move our data?
No, source systems stay in place. The graph references them. Data lands in the graph either through streaming CDC (change-data-capture) or scheduled ETL, and the graph carries the identifiers and relationships, not a copy of every record. Your source of truth stays your source of truth.
Related reading
The 30-Minute Graph Audit
For data and platform leaders who suspect a knowledge graph is the answer but cannot yet prove it to a CFO. You leave with a written architecture call, not a proposal.
The Guaranteed Pilot
If the audit turns into a build, we agree the target in writing before any work starts: a hallucination rate, a latency budget, or a workflow acceptance criterion. Miss it inside six weeks and the pilot is free. Full methodology on /proof.
If similarity search over a document pile already answers your questions, we will tell you that in writing and stop there, with no proposal to follow.