AG
Agentic Giants

AI & Machine Learning

Enterprise Knowledge Graph Consulting

Ground your AI in a knowledge graph your compliance team can audit. Neo4j, Amazon Neptune, RDF: deployed in your VPC, in weeks.

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What is enterprise knowledge graph consulting?

The Answer

Enterprise knowledge graph consulting is the design and implementation of a connected data layer (on Neo4j, Amazon Neptune, or RDF) that grounds AI systems in your organization's verified facts. It replaces guesswork with a queryable model of your entities and relationships, so LLM answers become accurate, explainable, and citable for regulated industries.

What you get

Three outcomes we commit to before we start.

01

Ontology before code

A senior architect models your domain (entities, relationships, constraints) before we write a line of Cypher. The ontology is versioned, reviewed, and becomes the contract every downstream system reads against.

02

Deployed in your VPC

Neo4j, Amazon Neptune, or Stardog inside your AWS/Azure/GCP boundary. Data never leaves. Every query is logged. Access controls map to your existing IAM.

03

IP transfer from day one

You own the graph schema, the ETL, the query library, and the documentation. When we exit, your team can operate it: no per seat licensing, no revenue share, no lock in.

See the system

From ontology to a queryable graph in your VPC

Not a slide deck: the architecture your team inherits and the traversal your analysts will actually run. Forward this to whoever signs off on the platform choice.

architecture: ontology to production
IP: 100% yours
YOUR VPC · YOUR CLOUDYOUR SYSTEMSData warehouseCRMERPDocumentsversioned · reviewedOntology & graphNeo4j / Neptunefit to your stackDeployed: your VPCIP transfers to your teamCONTROLSVersioned schema · IAM mapped access · sits by your warehouseNO PER SEAT LICENSING: NO LOCK IN
1

Model

ontology first

2

Build

Neo4j / Neptune

3

Hand off

IP is yours

query console: what traversal looks like
6 to 10 weeks to first graph

Query

Which entities are within 3 hops of this sanctioned party?

4 entities found within 3 hops: 2 shell companies [1], 1 shared address [2], 1 shared director [3]. A relational query needs 6+ joins; this took one traversal.

[1]entity_graph.ownership · 2 hops out

[2]kyc_registry.addresses · shared record

[3]corp_filings.directors · cross referenced

✓ ontology v3.2 · versioned3 hop traversal, not 6+ joinsIP: yours, no lock in

The Guaranteed Production Pilot

Fixed scope · Written target

A production Knowledge Graph Consulting system in your VPC: audited, documented, owned by your team.

Not a slide deck and not a sandbox demo: a working Knowledge Graph Consulting deployment inside your own cloud boundary, mapped to your compliance controls and handed over with the schema, the eval harness, and the runbook.

Speed

Architecture and success criteria signed off in week one. First working slice running in your environment inside 30 days.

Zero effort

Fully done for you. Our senior squad owns ontology, build, evals, and compliance mapping: your team reviews and signs off, nothing more.

Risk reversal

Fixed scope, fixed price, and a measurable success target agreed in writing before we start. Miss the target and you don't pay for the pilot.

Request a scoped pilot

Related services

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Give your AI agents governed, auditable access to your systems through custom MCP servers. Built by the team that shipped 10 production MCP servers for Optevo: deployed in your VPC, in weeks.

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Generative AI Development

From prototype to production: LLM engineering that survives real users, real regulators, and real budgets.

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Industries we serve with this

Where Knowledge Graph Consulting lands in production.

Industry

Healthcare AI Consulting

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Industry

Fintech AI Development

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Service FAQ

People also ask about knowledge graph consulting.

Relational databases are optimized for aggregating rows by key. Knowledge graphs are optimized for traversing relationships: 'find every customer within 3 hops of this sanctioned entity'. When your queries involve many joins and variable depth paths, a graph is 10 to 100× faster and orders of magnitude clearer to reason about.

Not sure which lane is yours?

Which lane needs knowledge graph consulting right now?

A 30 minute call and we'll tell you whether this service or a different starting point fits your team best.

Recommended01

Enterprise

Fortune 500 & Regulated Industries

90% fewer hallucinations · audit ready
Request Confidential Briefing
02

Scale ups

Founders & High Growth Product Teams

First release in 6 weeks · 0 data hires
Get my Velocity Audit
03

Brands

CMOs, D2C, Franchises & Retail

One customer graph · +30% AOV
Get my Intelligence Audit
04

Small Teams

Trades, Agencies, Founders & Solopreneurs

3× qualified leads · on autopilot
Get my Rapid AI Audit
Every lane starts with a no-obligation conversationYou keep the findings either way

Enterprise Knowledge Graphs, What They Are and When to Use One

What is an enterprise knowledge graph?

An enterprise knowledge graph is a connected data layer that models an organization's entities (customers, products, transactions, policies) and the relationships between them as a queryable graph, rather than as isolated rows in separate tables. It gives AI systems a single, governed source of verified facts to reason over, so answers trace back to real data instead of a model's best guess.

Unlike a data warehouse (built for aggregating rows) or a vector store (built for fuzzy similarity), a knowledge graph is built for traversal: following explicit relationships across many hops with full provenance. That combination of traversal and provenance is what makes it the grounding layer for GraphRAG and auditable AI in regulated industries. Agentic Giants builds enterprise knowledge graphs on Neo4j, Amazon Neptune, or RDF, deployed in your VPC in 6–10 weeks.

Knowledge Graph vs. the Alternatives

Why enterprises reach for a graph when the questions span systems and have to be defensible.

CapabilityWarehouse / Vector StoreEnterprise Knowledge Graph
Data shapeRows in tables, or opaque embeddingsEntities plus explicit, typed relationships
Best atAggregation, or fuzzy similarity searchMulti-hop traversal with full provenance
AI groundingChunk retrieval, no source-level provenanceFact-level grounding, every answer cites its source
Multi-hop queriesExpensive joins, degrade with depthNative traversal, 10–100× faster
ExplainabilityOpaque, hard to reconstruct a decisionAuditable path from question to answer
Regulated useHard to defend in reviewSOC 2 Type II delivery, deployed in your VPC

Why enterprises choose knowledge-graph consulting over building in-house

Shipping a production knowledge graph requires three disciplines most teams don't have together: ontology modeling, graph data engineering (ETL from messy source systems), and GraphRAG integration. Getting the ontology wrong early means rework across every downstream system, so the modeling is where a specialist pays for itself.

Agentic Giants models the ontology before writing a line of Cypher, deploys inside your VPC, and transfers the schema, ETL, query library, and documentation so your team owns and operates it, no per-seat licensing, no revenue share, no lock-in. The senior team brings 12+ years of engineering experience and has delivered 587+ projects across fintech, healthcare, retail, and SaaS.