The document discusses the utilization of graph technology, specifically by Neo4j, to transform businesses across various sectors including retail, banking, and healthcare through enhanced data relationships and insights. It highlights the importance of the graph model in driving operational improvements and the challenges organizations face in implementing generative AI projects. Additionally, it introduces 'Graphrag,' a method combining knowledge graphs with language models for improved accuracy and effectiveness in AI responses.

1. Bienvenue au
Graphtalk Paris

2. The Art of the Possible
With
Graph Technology
Adrien Boulad
Head of Southern Europe, Neo4j

3. Relationships are all
around us.
3

4. Relationships help us
understand the world.
4

6. 6
This is our Big Idea, and we
encode that idea in
software.

7. 7
With that software,
our customers
change the world.

8. Mission to Mars
Curing Cancer
Protecting
Democracy

9. The power of
the graph model

10. The power of
the graph model

11. The power of
the graph model

12. The power of
the graph model

13. The power of
the graph model

14. The power of
the graph model

15. The power of
the graph model

17. Graph is flexible

18. Employees
Network & Security
Product
Customers
Transactions
Process
Suppliers
Graph is insightful
Employees
Network & Security
What’s important?
Process
Suppliers
What’s unusual?
Product
Transactions
Customers
What’s next?

19. 19
Trusted by 75 of the
8 / 10
Top Retailers
10 /10
Top Automakers
20 / 20
Top US banks
3 / 5
Top Aircraft Manufacturers
7 / 10
Top Telcos
3 / 5
Top Hotel Groups
8 / 10
Top Insurance Companies
5 / 5
Top Pharmaceuticals

20. Employees
Network & Security
Suppliers & Partners
Product
Customer
Finance
Process
The Seven Graphs
of the Enterprise

21. Supplier
Inventory
Product
Suppliers
Route Planning & Optimization
Real-time Shipment Tracking
Inventory Planning
Risk Analysis
Uncover patterns in your
supplier data

22. Supplier
Inventory
Product
Suppliers
Uncover patterns in your
supplier data

23. Supplier
Inventory
Product
Suppliers
Uncover patterns in your
supplier data
Estimated annual
Economic savings
Estimated traffic
congestion reduction
10% £570M

24. Uncover patterns in your
Transactions
Product
Transactions
Anti-money Laundering
Circular Payments
Fraud Detection
Customer
transactions data

25. Uncover patterns in your
Transactions
Product
Transactions
Customer
transactions data

26. Uncover patterns in your
Transactions
Product
Transactions
Customer
transactions data
Approves or rejects applications in
less than
two seconds
20% reduction in credit application
fraud

27. Talent Learning & Development
Career Management
Skills Management
Orgs (who is who)
Job Search Account/Identity Control
Reputation Scoring
Threat Detection
Access Control
Zero Trust
Route Planning & Optimization
Real-time Shipment Tracking
Inventory Planning
Risk Analysis
Product Recommendations
New Product Introductions
Product Customizations
Product Inventory
Product Pricing
Bottleneck Identification
Process Improvement
Process Automation
Process Monitoring
Recommendations
Loyalty Programs
Churn Prevention
Customer Offers
Dynamic Pricing
Intelligent Ads
Anti-money Laundering
Circular Payments
Fraud Detection
Employees
Network & Security
Suppliers
Product
Customers
Finance
Process
Connect Your Seven Graphs
to Drive Transformation

28. 28

29. Neo4j Inc. All rights reserved 2024
$11 $18 $37 $64
$128
$219
$340
$457
$611
$790
$981
$1,166
$1,361
2% 2%
4%
6%
7%
8%
10%
11%
12% 12%
0%
5%
10%
15%
20%
25%
30%
$-
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
2020 2022 2024 2026 2028 2030 2032
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

30. Why are LLMs hitting a
barrier?
30

31. Language
Statistics
Creativity

32. Knowledge
Facts
Context
Language
Statistics
Creativity

33. Knowledge
Facts
Context
Language
Statistics
Creativity
KG LLM

34. Introduction to GraphRAG

35. GraphRAG is RAG where the R path includes a
Knowledge Graph.
What is GraphRAG?

37. The core GraphRAG pattern is very simple, yet powerful.
1. Do a vector search to find an initial set of nodes
2. Traverse the graph around those nodes to add context
Optional: Rank the results using the graph and pass the top-k documents to the LLM
GraphRAG Retrieval Patterns

38. The Benefits of GraphRAG

39. Neo4j Inc. All rights reserved 2024
Design in prg
3X
A Knowledge Graph improves
the accuracy of LLM responses
by 54.2%, an average of 3x
Higher Accuracy
1.

40. Feedback from an AI
Engineer
Easier Development
2.
Redacted

41. Human View of an Apple LLM + Vector View of an Apple
Easier Development: Why
2.

42. Knowledge Graph View of an Apple LLM + Vector View of an Apple
2. Easier Development: Why

43. Easier Development: Why
Transparent & Explainable Opaque & Implicit
AI Engineer: "I actually already fixed a couple of bugs thanks to this!”
2.

44. Detour! Knowledge Graph Creation
Unstructured data
Typically PDFs or other text
documents
Mix of structured &
unstructured data
Structured data with long-
form text
Structured data
Structured data with short
text values
Watch This Space!

45. Explainability & Governance
3.

46. The Benefits of GraphRAG
1. Higher
Accuracy
2. Easier
Development
3. Explainability
& Governance

47. Relationships are everywhere
And enable accurate & explainable AI
They power transformative use cases

48. Connect