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Rivac AI Labs
Engineering & Software Development

Knowledge Graph Engineer

Unstructured documents hide relationships that flat text search and vanilla RAG can't reason over, who reports to whom, which contract supersedes which, how one entity connects to another three hops away. We extract entities and relationships from your documents and structure them into queryable knowledge graphs in Neo4j or LangGraph, then wire that graph into retrieval so your AI features can answer relationship questions, not just similarity-matched ones. This is the right call when flat RAG is giving shallow or wrong answers to questions that require connecting multiple facts. You get a graph your team can query directly, plus the extraction pipeline that keeps it current as new documents arrive.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

  1. 1

    Identify the entity types and relationships that actually matter for the questions you need answered.

  2. 2

    Build an extraction pipeline against a document sample and load it into a pilot graph to test query quality.

  3. 3

    Review the graph structure and sample query results with your team, refining entity resolution and schema.

  4. 4

    Scale extraction across the full corpus and wire the graph into your RAG or application layer.

What You Get

Deliverables from this engagement

  • Populated knowledge graph (Neo4j or equivalent)
  • Entity and relationship extraction pipeline
  • Query interface or API for graph-enhanced retrieval
  • Schema documentation for maintaining the graph over time

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Entity Extraction

  • Source Documents
  • LLM Based Entity and Relation Extraction
  • Raw Triple List
  • Manual Review of Extracted Triples

Lightweight Graph Store

  • Single Node Neo4j Instance
  • Basic Deduplication Pass

Query Time

  • User Question
  • Cypher Query Generation
  • Graph Traversal Result
  • LLM Composes Answer
  • Answer Returned to User
  • Log Missed Relationships for Next Batch

Cloud-Native / Managed Services Architecture

Managed Extraction Pipeline

  • Document Ingestion Service
  • LLM Extraction Service for Entities and Relations
  • Schema Validation Layer

Managed Graph Database

  • Managed Neo4j Aura Instance
  • Entity Resolution and Merge Service
  • Graph Backup and Snapshot Service

Graph Enhanced Retrieval

  • Vector Store for Node Embeddings
  • Hybrid Graph and Vector Retrieval
  • LangGraph Orchestration Layer

Serving

  • LLM Generation With Graph Context
  • API Gateway
  • Client Application

Event-Driven & Automation-First Architecture

Update Triggers

  • New Document Event
  • Document Updated Webhook
  • Scheduled Full Resync Job

Automated Extraction Pipeline

  • Extraction Queue
  • Entity Extraction Worker
  • Relation Extraction Worker
  • Graph Upsert Worker

Live Graph

  • Neo4j Graph Database
  • Change Log of Graph Mutations

Automated Downstream Refresh

  • Trigger Vector Re Embedding for Changed Nodes
  • Invalidate Cached Answers
  • Notify Dependent Services

Enterprise-Grade & Compliance-Ready Architecture

Governed Extraction

  • Source Documents With Classification Labels
  • Extraction Pipeline With Provenance Tagging
  • Human Curator Review Queue

Secured Graph Store

  • Access Controlled Neo4j Cluster
  • Row and Node Level Permissions
  • Encrypted at Rest and In Transit

Reliability

  • Replicated Graph Nodes
  • Point in Time Graph Backups

Audit and Compliance

  • Query Audit Log per User
  • Immutable Change History
  • Compliance Dashboard
  • Data Retention Policy Enforcement

AI-Augmented, Human-in-the-Loop Architecture

AI Extraction

  • Documents Ingested
  • LLM Extracts Candidate Entities and Relations
  • Confidence Score per Triple

Human Curation Gate

  • Confidence Above Threshold
  • Auto Accept High Confidence Triples
  • Human Curator Reviews Low Confidence Triples
  • Curator Approves Triple
  • Periodic Sample Audit of Auto Accepted Triples

Graph Write

  • Approved Triples Written to Graph
  • Rejected Triples Logged for Model Feedback

Serving

  • Graph Enhanced RAG Query
  • Answer With Cited Graph Path

Integration-Heavy / Ecosystem-Connected Architecture

Connected Sources

  • Contract Management System
  • HR and Org Chart System
  • CRM Records
  • Internal Wikis
  • Document Management System

Knowledge Graph Core

  • Extraction and Resolution Pipeline
  • Neo4j Knowledge Graph
  • Incremental Update Watcher

Ecosystem Consumers

  • Graph Enhanced RAG Assistant
  • Business Intelligence Tool
  • Compliance Query Interface
  • Internal Search Portal

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Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Knowledge Graph Engineer

Most engagements like this start as a $500–$2,500 pilot, see full pricing.

Questions? Book a free call