Graphs & GraphRAG - Essential Ingredients for GenAI

Data+AI 2025 | www.factor-insights.com
Graphs & GraphRAG
Essential Ingredients
for Enterprise GenAI
Thursday | March 27
Factor Data+AI Day
Events Centre, Collins Square Melbourne
Emil Pastor
Head of Solutions
Engineering A/NZ
Neo4j

"Science Finds, Industry Applies, Man Conforms”
“Sociotechnical” Principles
Motto of the 1933 Chicago World’s Fair

AI requires a Holistic Approach1
IT Infrastructure & Management
Data Engineering & Quality Management
Machine Learning & Analytics
Project Management
Organisational Change
Industry Transformation
Regulation
Principles & Philosophy (e.g. Ethics)
1 – T. Evgeniou, INSEAD

Your business
is agile and
dynamic

But your data
is locked in
rows and
columns

Valuable
patterns are
hidden in
your data

Model your
data like
your
business

Data, meet graph
Employees
Network & Security
Suppliers
Product
Customers
Transactions
Process

Employees
Network & Security
Services
Citizens
Transactions
Process
Suppliers
Graph is insightful
Employees
Network & Security
What’s important?
Process
Suppliers
What’s unusual?
Services
Transactions
Citizens
What’s next?

Generative AI races
toward $1.3 trillion in
revenue by 2032
Generative AI Revenue
Generative AI as a % of Total Technology Spend However, 71% of
organizations are
stuck piloting
GenAI projects
2024 IBM CEO Survey
Bloomberg Intelligence

85% of IT leaders
cite data quality as
their top AI
challenge
KPMG AI Quarterly Pulse Survey 2025
100 senior executives from $1B+ revenue
companies
It’s a data management issue!
Lack of Standardisation
Scale of Query Diversity
Data Organization
AI needs data organized in
ways it can effectively access
AI requires high-quality
harmonized data to be reliable
AI demands rapidly accelerated
question-to-query execution

Flexible Standardised
Enables AI to grasp meaning
and access data across
sources
Supports diverse query types
and evolving data needs
Maintains consistent
understanding at scale
What makes data AI-ready?
Data exposes
entities and
relationship
understanding
Adapts to new
patterns, structures
and access logic
Consistent,
governed
data foundation
Contextual

Connected Data
Node
Property Graph Data Model
Person
Name:Terry
Person
Name:Sam
Car
Brand:Volvo
Since:10/Jan/11
DRIVES
OWNS
SISTER_OF
BROTHER_OF
Since:10/Feb/11
Relationshi
p
Property
= AI-ready data
Knowledge graphs

Flexible Standardised
Entities and their connections
are built into the data model
Evolve your model while
queries keep running
Consistent meaning across
all your data sources
Relationships are
treated as primary
data structures
Schema grows &
adapts to meet
evolving business
needs
Expressive data
model provides
a common
language
Contextual
= AI-ready data
Knowledge graphs

GenAI Applications
LLM Orchestration inc. retrievers, tools & agents
AI & KG Architecture
LLM Platform
Models & Reasoning Engine
Enterprise Data Platform & External Databases
mapping unstructured + structured data
Knowledge Graph

Ordinary RAG

GraphRAG: RAG with graph
It’s actually quite broad…
GraphRAG is Retrieval
Augmented Generation
(RAG) using Knowledge
Graphs
Improves GenAI by taking
advantage of rich graph
data structures.
Community summaries &
graph enrichments
Graph enhanced vector
search
Dynamic graph query
generation
+ more: graph vectors,
parent-child retrievers, etc.
Retrieval Patterns

AI Evolution & Going to Production
Graphs are the next
step – a central data
component for the
AI stack
Generative models
RAG
Agents
Knowledge Graph
GraphRAG
Trust | Performance | Extensibility

Language
Statistics
Creativity

Knowledge
Facts
Context
Language
Statistics
Creativity

Benefits of
25

GraphRAG
Performance
RAG
Performance
Lettria Analysis1
81.67% 57.50%
Writer Knowledge Graph2
(RobustQA Benchmark)
86.31% 32.74%–75.89%
RAG vs. GraphRAG: Multi-hop
Question Answering3
77% 66%
GenUI Experiments
(MultiHop-RAG Dataset)4
Successfully answered
complex, multi-step queries
Struggled integrating data
from multiple sources
GraphRAG delivers up to
3x higher accuracy than
traditional RAG, with
better multi-hop
reasoning for context-
rich AI applications.
1) https://writer.com/engineering/rag-benchmark/ 2) https://www.lettria.com/blogpost/vectorrag-vs-graphrag-a-convincing-comparison
3) https://arxiv.org/abs/2502.11371 4) https://www.genui.com/resources/graphrag-vs.-traditional-rag-solving-multi-hop-reasoning-in-llms
1.) 3x Higher Accuracy
1

2. Ease of Development
Natural language description: “Apples & oranges are both fruits”
2

X
Customer
Service
Doctor
Social Security
Number
Social Security
Number
Patient
Bob
Phone
Number
Health
Diagnosis
2. Explainability
3

Benefits of GraphRAG
Higher
Accuracy
Easier
Development
Improved
Explainability

What does this look
like in
30

Internal
Documentation
Wikis
Enterprise
Systems
Klarna transforms knowledge access
with GraphRAG
HR Systems

Internal
Documentation
Wikis
Enterprise
Systems
Klarna transforms knowledge access
with GraphRAG
HR Systems
Daily queries processed
250K Employee questions
answered in first year
2,000
85% Employee adoption

Klarna deploys Neo4j graph
to fuel AI initiatives
What we’ve started using extensively in-house is Neo4j and
graphs. And we’ve also looked at how people collaborate on
building great information.
And then on top of that we have Kiki, who then explores
that information and brings it to life. So we can go and ask
Kiki about anything!
We’re seeing that it’s having a tremendous impact on productivity
internally. So Kiki is basically our own internal chatbot based on that
growing internal knowledge graph.
- Sebastian Siemiatkowski, Co-founder & CEO

34
Internal
Forecasts
Analyst Reports
Gaming Data Sales
Subscriptions Marketing
Competitor
Launches
Developmen
t Decisions
Revenue
Promotions
Player
Engagement
Pricing
Strategy
Market
Condition
s
10x faster 92% 150+
Time-to-insight
reduction
Reduction in
analyst time on
routine
requests
Business users
daily
When millions in revenue hang in the balance - knowledge graph powered agents prove invaluable
Gaming Giant Transforms Analytics with
GraphRAG
● Maps structured & unstructured
data from multiple sources
● Semantic context centralized &
controlled in the data layer -
Unmappable with traditional databases
● Speed, scale, traceability & most
importantly trust & confidence in
enterprise AI responses
AI Agents

Architectur
e
Documents
Design
Diagrams
Technical
Specifications
VM02 turns docs into design intelligence
Enterprise
Metadata

Architectur
e
Documents
Design
Diagrams
Technical
Specifications
VM02 turns docs into design intelligence
Enterprise
Metadata
Time reduction
20%
↑ Driving up productivity
↑ Enhancing quality of deliverables
↑ Enabling faster delivery

37
Intelligence
Data
Financial
Data
Unlocking defense and energy insights with GraphRAG
Operational
Records

38
Operational
Records
Intelligence
Data
Financial
Data
Market for AI
in energy sector
Analyst workload
reduction
50% $13B
Unlocking defense and energy insights with GraphRAG
FROM DATA TO DECISIONS

The Necessity of Graph Databases
Agile Development Query Performance Lower Total Cost of
Ownership
Fast implementation and
iteration with a flexible
schema
High-speed retrieval
across connected data
Better results with
smaller models through
contextual understanding
Real-Time
Transactional & ACID with
access controls, high
availability, durability, etc.
()-[*]->()

Graph DB
Agility, Speed, Low TCO & Real-Time
AI Evolution & Going to Production
Graphs are the next step - a central data component for the AI stack
Trust | Performance | Extensibility
Knowledge Graph
Context, Flexibility,
Standardisation
GraphRAG
Accuracy, Explainability,
Governance

The Future of
AI Adoption is Connected.
How will you harness
Graph + AI to drive
smarter applications?

Thank you!
Emil Pastor | emil.pastor@neo4j.com
Head of Solutions Engineering, A/NZ
Catch us on the booth area

Graphs & GraphRAG - Essential Ingredients for GenAI

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