Fraud, AML, and regulatory intelligence
- Multi-hop transaction analysis surfaces fraud rings that row-based queries miss
- Beneficial ownership, counterparty exposure, and regulatory classification in one query
Databricks · Neo4j Accelerator · 6-week engagement
Give Databricks the structure it needs to reason.
Vivid deploys a production-ready semantic ontology layer that connects Neo4j’s knowledge graph to your Databricks lakehouse in six weeks, providing the structured domain intelligence that AI needs in order to reason correctly from day one.
Book time with us
- Fixed 6-week engagement
- Databricks Partner Certified
- AWS · Azure · GCP
The Semantic Ontology Accelerator is a fixed six-week Vivid engagement that connects a Neo4j knowledge graph to a Databricks lakehouse. It designs the domain ontology, builds the Delta Lake to graph pipeline, extends Unity Catalog governance into the graph, and wires GraphRAG so AI agents retrieve auditable, explainable context.
- Accelerator
- Semantic Ontology Accelerator
- Leads with
- Technology lens
- Also draws on
- Operating Model
- Deliverables
- 4 work products, formatted for internal use
- Ends with
- AI agent integration
Platforms do not make AI useful on their own. Structure does.
Most Databricks investments stall between technical capability and domain-specific AI output. Vivid closes that gap by connecting your lakehouse to a knowledge graph, so that AI agents can reason correctly about how your business actually works.
Large language models have no inherent understanding of enterprise relationships, and that gap surfaces as opaque outputs that cannot be explained or traced. GraphRAG closes it: rather than retrieving fragments of similar text, it traverses explicit relationship paths from a connected knowledge graph, which makes outputs explainable, auditable, and grounded.
Knowledge graph design
Domain entities, relationship types, and property schemas mapped from your business model, and built to your data contracts rather than to a template.
Lakehouse integration
Delta Lake synced to Neo4j through incremental pipelines, with Unity Catalog governance extended into the graph. Your existing contracts are honored rather than replaced.
AI-ready query layer
GraphRAG pipelines, LangChain and LlamaIndex connectors, and MLflow tracing, so that your agents retrieve auditable context and your team can see the reasoning behind it.
Six workstreams leading to one production-ready outcome.
Every engagement delivers the complete semantic layer, with no carve-outs and no scope surprises, for a fixed fee confirmed before we start.
- 01
Environment and connectivity
Databricks workspace audit and environment configuration, Neo4j AuraDB provisioning or self-managed setup, and Spark to Neo4j connector configuration and validation. Covers network security, credential management, and cloud provider integration across AWS, Azure or Google Cloud.
- 02
Domain ontology design
Business entity and relationship mapping workshops, followed by taxonomy design across node labels, edge types, and property schemas. Produces ontology documentation and a version-controlled schema registry, aligned to existing data contracts and the business glossary.
- 03
Lakehouse to graph pipeline
Delta Lake extraction and transformation into the graph schema, with incremental sync and change-data-capture patterns. Includes data quality validation, error handling, and pipeline orchestration through Databricks Workflows or Airflow.
- 04
Semantic query layer
A Cypher query library for common domain patterns, natural language to Cypher translation scaffolding, and an LLM-ready context packaging and retrieval API, with embedding integration for vector-augmented graph retrieval.
- 05
Unity Catalog governance
Data lineage registration for graph entities, role-based access control integration, audit logging, and compliance tagging, with data classification aligned to Unity Catalog standards.
- 06
AI agent integration
LangChain, LangGraph, and LlamaIndex connector setup, with the GraphRAG pipeline wired through GraphCypherQAChain. Includes MLflow tracing for agent observability and evaluation, Databricks Model Serving endpoint deployment, and playbook and runbook handoff.
Deliverables
Where the semantic layer pays for itself.
Knowledge graphs compound in value the more interconnected a domain is, and these are the sectors where the layer delivers the highest return.
Clinical AI and care pathway intelligence
- Patient journey graphs surface care pathway deviations across disconnected EHR systems
- Ontology-driven cohort identification accelerates clinical trial matching
Supplier risk and logistics optimization
- Multi-tier supplier dependency graphs identify single points of failure before disruption
- Procurement and logistics relationships connected across ERP, WMS, and IoT streams
Network operations and customer intelligence
- Network topology graphs power root-cause analysis and outage impact assessment
- Customer-to-service relationship models improve churn prediction and cross-sell
Optional add-ons
- Multi-domain ontology expansion
- Real-time CDC architecture
- Custom Cypher agent development
- Executive data literacy workshop
Best for
Fixed scope, agreed in advance, delivered in six weeks.
Kickoff to handoff runs six weeks, with scope and fee agreed before we start.
Kickoff to handoff
A fixed engagement rather than an open-ended programme. The complete semantic layer is in production at week six.
Agreed before we start
The six workstreams constitute the engagement, with no carve-outs and no scope surprises once work begins.
No on-site presence required
The engagement is delivered remotely across AWS, Azure or Google Cloud, working against your existing Databricks workspace and data contracts.
Available where a domain needs more
Multi-domain ontology expansion, real-time change-data-capture architecture, custom Cypher agent development, and executive data literacy workshops.
More accelerators
This engagement stands alone, and so does every other one.
Each accelerator is scoped and time-boxed, and none requires the others to deliver value. Run them in sequence and the governance and patterns compound.
Common questions
Answers to the questions we are asked most.
What is the Semantic Ontology Accelerator?
A fixed six-week Vivid engagement that connects a Neo4j knowledge graph to a Databricks lakehouse, delivering a production-ready semantic ontology layer with GraphRAG wired into your AI agents.
Why does AI need a knowledge graph?
Large language models have no inherent understanding of enterprise relationships, so they produce outputs that cannot be explained or traced. GraphRAG traverses explicit relationship paths in a connected graph instead of retrieving fragments of similar text, which makes the output explainable and auditable.
How long does it take and what does it cost?
Six weeks, fixed scope, and fixed fee, agreed before the engagement starts. Delivery is fully remote.
Which clouds does it support?
AWS, Azure, and Google Cloud, against Neo4j AuraDB or a self-managed Neo4j deployment.