Become an agentic enterprise your auditors can sign off.
Knowledge graph systems on Neo4j that ground every AI answer in your data. Production hardened GraphRAG with measurable hallucination reduction in controlled enterprise testing. Built by a senior team, measured in production: deployed inside your own cloud.
✓No obligation. In 30 minutes we’ll show you 3 compliance or architecture risks blocking your next AI release.
15+ enterprise clients · 95% extend engagement · Neo4j expertise
What is an agentic enterprise?
An agentic enterprise is one where AI does not only analyse but executes: software plans a sequence of steps, acts on real systems of record, and leaves an audit trail a regulator can follow. The distinction is not model quality. It is whether the institution has the grounding, the permission scoping and the evidence trail to let software act rather than advise.
That is why most programmes stall. A demo built on unconnected data is convincing in the room and fails the first audit question, because nothing can show where an answer came from. Becoming an agentic enterprise is a governance problem before it is an engineering one, and the sequence below is the order that survives review.
The words on the RFP
Agentic AI, AI agent, autonomous agent: what each one means.
The agentic vocabulary is genuinely muddled right now, and vendor material uses these terms interchangeably. If you are reading proposals, these are the distinctions worth holding onto.
- Agentic enterprise
- An organisation where AI executes inside real systems rather than only analysing and advising, with the grounding, permission scoping and audit trail that lets software act on its own.
- AI agent
- A system that plans a sequence of steps, calls tools to carry them out, and checks the result against a goal. The planning and the tool calls are what separate it from a single model prompt.
- Agentic AI
- The broader property of acting rather than answering. Used loosely in vendor material: worth asking any supplier which actions their system actually executes, against which systems of record, and what happens when it is wrong.
- Grounding
- Constraining a model to answer from retrieved facts you own instead of from training recall. Without it, every downstream control is defending against output nobody can trace.
- Knowledge graph
- Your entities and the verified relationships between them, stored so a query can traverse them. The grounding layer an agentic system reasons over, and the reason an answer can cite a record rather than a paragraph.
- Provenance
- The record of where a fact came from and which transformation produced it. What turns an answer into evidence, and the first thing an auditor asks for.
- Policy layer
- The component that validates a proposed action against rules before execution: permissions, limits, segregation of duties, jurisdiction. It sits between the agent's decision and the system of record.
- Model Context Protocol (MCP)
- A standard for exposing tools to an agent as named, typed, permission-scoped calls instead of handing it raw credentials. The interface where governance is practical to enforce.
- Evaluation harness
- A graded test set run before and after every change, scoring groundedness, citation precision and refusal behaviour. The difference between claiming accuracy and demonstrating it.
- Model inventory
- A register of every model in use with its owner, purpose and risk tier. Required by the EU AI Act and the NIST AI RMF, and the artefact most programmes discover they are missing during review.
- Human oversight
- A named person able to review, override or halt an automated decision, with the authority and the information to do it. A regulatory expectation, not only a design preference.
- Audit trail
- A tamper-evident log of what the system did, on what evidence, under whose approval. The deliverable that decides whether an agentic programme survives its first review.
The three reasons enterprise AI programmes stall.
These are the problems enterprise leaders bring us most often. If any of them sting, the briefing will pay for itself.
Data is your biggest blocker, not your team
Your AI prototypes work in the lab but hallucinate on real, fragmented enterprise data. Compliance teams are skeptical: and they’re right to be.
Hiring isn’t the answer
You can’t hire a full graph engineering team overnight, and your existing engineers don’t have the specialized knowledge. Six months of recruiting buys you a bench, not a system.
The board is losing patience
AI initiatives are critical to your market position: and they’ve been stuck in the R&D phase for quarters while competitors ship.
Conservative math: every quarter of delay costs $650K+ in stalled initiative burn, manual verification tax, and risk exposure , more than the entire system that fixes it.
What an agentic enterprise actually requires
Everything required to take AI from “blocked by compliance” to “signed off and in production”: delivered as one system, not a slide deck.
Enterprise ontology & knowledge graph architecture
Your entities, relationships, and business rules modeled into one governed graph: the single source of truth your AI reasons over.
Production GraphRAG pipeline + evaluation harness
Retrieval that traverses the graph instead of guessing from embeddings, with an eval suite that measures hallucination rate against your baseline: continuously.
Compliance & citation layer
Per answer source citations, reasoning traces, human in the loop approvals, and immutable audit logs. The artifact your risk committee actually signs off.
In VPC security deployment
Everything deployed inside your cloud with SSO/RBAC integration and zero data egress. Verified at the network level by your own team.
Team enablement & full IP handover
Documentation, runbooks, and pairing sessions so your engineers own and extend the system. No lock in, no black boxes.
Confidential 30 minute architecture briefing + written recommendation
Three documented savings opportunities of $50K+ each, mapped to your architecture. Yours to keep whether or not we ever work together.
Combined standalone value: $495K+ · Your first step costs $0 and 30 minutes
Hit the agreed hallucination reduction target on your data: or the pilot is free.
Before the pilot starts, your team and ours agree the evaluation baseline and the reduction target in writing. We measure on your data with your people watching. If we miss the performance targets, the pilot is free (no exceptions) and you keep every finding.
Built for the world’s most regulated industries
Your data is your competitive advantage. We treat it that way: compliance is architected from day one, not bolted on.
Private AI deployment
Your data stays on your infrastructure. No training on your proprietary data. Ever.
SOC 2 Type II delivery
Systems audited to SOC 2 Type II controls, security, availability, confidentiality. Attestation issued against your deployment by your auditor of record.
HIPAA ready architecture
Designed to handle protected health information (PHI) with full audit trails.
GDPR compliant data handling
Right to forget, data portability, and strict access controls built in.
Audit ready explainability
Every AI answer traceable to source documents with full provenance.
Data sovereignty
Deploy regionally or in your specific cloud: AWS, Azure, or GCP.
The bottom line: you get the competitive advantage of AI without compromising on security, privacy, or compliance.
Every answer cited: or it doesn’t ship.
Compliance grade AI, grounded on your knowledge graph. Each response carries a traceable evidence path back to source systems: the artifact your auditors actually accept.
Your risk committee approves. In weeks, not quarters.
Citations, audit logs, and human in the loop controls are built in from day one: so review boards say yes to production instead of killing another pilot.
Why Fortune 500 leaders choose us over a Big 4 build
We are Neo4j experts
We don't just implement knowledge graphs: we architect them for scale, performance, and accuracy.
We ship, we don't consult
You get a working system, not a PowerPoint. Average time to production: 6 weeks.
We handle compliance
SOC 2, HIPAA, GDPR, and private AI deployments are standard in our delivery.
You own everything
Built in your repo, on your infrastructure. No vendor lock in. Ever.
A reference architecture, not a discovery workshop.
We don’t open with a 40 page intake form. Your request lands; a senior architect and compliance lead reply with a concrete shape, drawn from every comparable system we’ve taken through a risk committee. Recommendations, not interrogation.
Sarah Chen · CTO, Meridian Bank · client
“Our AI copilot is blocked in model risk review. Legal won’t approve anything it can’t verify.”
Advisory · Agentic Giants architect + compliance lead
Drawn from Fortune 500 financial services engagements
For a business like yours, we recommend:
- GRAPH
GraphRAG layer on your existing data fabric no migration: the graph connects your 14 systems where they are.
- EVAL
Measurable hallucination reduction eval baseline agreed with your model risk team, tracked in production.
- SEC
Private AI deployment SOC 2 / HIPAA-ready architecture: no data ever leaves your infrastructure.
- LOG
Audit logging for every interaction provenance on every answer, regulator ready export.
Drawn from our knowledge base of past engagements: the same graph technology we sell.
Briefing to sign off in 90 days
Staged commitment: you decide again at every milestone, with evidence in hand.
- Day 0
Confidential briefing call
30 minutes with a senior architect. We map where your AI answers come from and where they'll fail review.
- Week 1
Findings report
Three documented savings of $50K+ each and a pilot scope with an agreed evaluation baseline.
- Weeks 2 to 6
Guaranteed pilot on your data
Graph + GraphRAG on a real use case, measured against the baseline. Miss the target, pilot is free.
- Weeks 7 to 12
Production & sign off
Hardened deployment in your VPC, compliance documentation, team handover. Regulators see citations, not vibes.
“Our compliance team went from blocking every AI feature to approving them: because now every answer has a source.”
Dan Nusinovich, Vice President of Product Development · Fintech (RYVYL) · Measurable hallucination reduction in production
Trusted by teams at
Selected client engagements. Some client names, deliverables and outcomes are under NDA and cannot be published; published case studies are here.
One honest constraint: every build is led by senior engineers, the same people on this page’s numbers, so we take on a limited number of enterprise engagements per quarter. The briefing costs nothing and commits you to nothing, but pilot slots are scheduled in the order briefings complete.
What boards and risk committees ask first
Three layers, in this order. A grounding layer so answers trace to your own verified facts rather than model recall. A governance layer so every action is permission-scoped, policy-validated and logged. Only then the agents themselves. Programmes that invert that order produce a demo that impresses the room and fails the first audit question, which is why most stall before production rather than during it.
Relevant client results
View all case studies →Ready to become an agentic enterprise?
One 45 minute confidential briefing with our senior architects. We’ll review your AI roadmap, data infrastructure, and compliance requirements: and deliver a written architecture recommendation. It’s yours either way.
✓ No obligation · ✓ The recommendation is yours either way · ✓ Pilot backed by the performance guarantee