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1. #9307712
Q&A

2. Revolutionising Customer
Experience: Neo4j Based GraphRAG
and GenAI for Hyper Personalisation
Emil Pastor, Head of Solutions Engineering, ANZ
Samko Yun, Sr. Solutions Engineer

3. Agenda
●Neo4j Graph Overview and How Customers Benefit from Neo4j
●Neo4j Graph Platform
●Improving Customer Experience using Neo4j Knowledge Graph
●Neo4j GraphRAG and GenAI Interactive Session
●Neo4j Resources
Neo4j Inc. All rights reserved 20253

4. Neo4j Graph Overview
Neo4j Inc. All rights reserved 20254

5. 6
Data in a Table (Relation)

6. 7
Data, meet Graph.
Model your data like your business,
with a connected view of dynamic
relationships

7. What are the elements in a Graph?
Node
Represents an entity in the graph
Property
Describes a node or relationship:
e.g. name, age, address
Label
Grouping of similar nodes
Relationship
Connect nodes to each other
ANNEMARK
Name: “Mark”
Role: Bank Staff
Mobile: [phone]
Location: “125 Queen Street, Auckland”
Date: 2025-02-11
Event Date:
2025-09-21
PERSON
PERSON
BANK
Event Date:
2025-12-01
EMPLOYEE
RETAIL BANK
W
O
R
K
S

A
T
HELP
KNOWS
V
I S
I T
E
D
Neo4j Inc. All rights reserved 20258
BANK

8. Top3 “Why” Native Graph DB and Analytics?
1 2
3
Neo4j Inc. All rights reserved 20259

9. Examples of How Customers
Benefit from Neo4j
Neo4j Inc. All rights reserved 202510

10. How Customers Benefits from Neo4j? (1/3)
Enterprise Search (LLM)Network Observability Real-time
Recommendation
Understand system
dependencies and de-risk
cloud migration
Knowledge Graph +
Graph RAG + Bloom
Achieved complete network
observability over cloud’s
dynamic network
Need a quick way to search for
repair & maintenance data
GDS + n10s + Graph RAG
(Enterprise Chatbot J.AI)
Queries return relevant
project information with a
snippet generated based on
NLU intent
Connect masses of complex
buyer & product data
Knowledge Graph +
Graph RAG
Matching historical and
session data to personalise
the contents in real-time
?
!
?
!
?
!

11. How Customers Benefits from Neo4j? (2/3)
Protein Discovery Farm Efficiency
Increase our knowledge of our
planet’s biodiversity (0.001%)
Knowledge Graph
(BaseGraph
™
)
Increase new protein
discovered by 50% every
month (2 years 1 month)
→

Analysing 60 years of complex
genomic data
Knowledge Graph +
GDS algorithm
Reduced the processing time
of real-time analysis from
hours to seconds
Deliver traceability reports to
the government (FSA) in time
Knowledge Graph +
Bloom visualisation
Real-time reports with
production data
?
!
?
!
?
!
Ingredient Traceability

12. How Customers Benefits from Neo4j? (3/3)
Customer 360
Distribution Centre
Optimisation
Knowledge Management
Disparate data sources delay
customer issue resolution
Knowledge Graph
Manual effort reduced from
weeks to 20 mins & Enabled
Real-time recommendation
Difficult to track “dark parcels”
Knowledge Graph
Use real-time events to
minimise and resolve
disruptions
Accessing decades of project
data siloed by department
Knowledge Graph
(Lesson Learned Database)
Saved more than $2 Million in
R&D cost on the mission to
Mars
?
!
?
!
?
!

13. Neo4j Graph Platform
Neo4j Inc. All rights reserved 202514

14. Neo4j Enterprise Platform Architecture
Neo4j Inc. All rights reserved 202515

15. Neo4j Inc. All rights reserved 202516
Fully-managed SaaS
Consumption-based pricing
Cloud-native
Self-service deployment
No access to underlying
infrastructure and systems
White-glove managed service
by Neo4j experts
Fully customizable deployment
model and service levels
Operate In own data centers
or Virtual Private Cloud
For private and hybrid
cloud, or on-prem
Bring your own license
Full control of your environment
Run in any cloud, in your account
Graph-as-a-Service Self-hosted
Cloud Managed
Services
Flexible Deployment Models

16. Neo4j Connectors and Integration Points
Neo4j BI
Connector
Apache Spark
Connector
Apache Kafka
Connector
Data Warehouse
Connector
Java Python .NETJavaScript Go
Neo4j Inc. All rights reserved 202517

17. Improving Customer
Experience using Neo4j
Knowledge Graph
Neo4j Inc. All rights reserved 202518

18. : Recommendation Engine
Challenge: Optimise walmart.com user experience
•Connect complex buyer and product data to
gain super-fast insight into customer needs
and product trends
•RDBMS couldn’t handle complex queries
Solution: Replaced complex batch process real-
time online recommendations
•Built simple, real-time recommendation
system with low-latency queries
•Serve better and faster recommendations
by combining historical and session data
Neo4j Inc. All rights reserved 202519

19. Example: Walmart’s Product Knowledge Graph
Neo4j Inc. All rights reserved 202520

20. X360 Recommendations
→
Organisational Data
Customer Data
Product Data
Event Data
3
rd
Party Data
Supply Chain Data
High Priority Questions:
●Which products or recipes should we
recommend to this customer?
●What complimentary items can be
recommend based what’s in their
basket? (realtime)
●What items are missing from their
basket, that we can recommend
based on prior shops?
●Which promotions should we
recommend on screen at self-service
checkouts?
●Which products can we recommend,
based on their browsing behaviour?
Neo4j Inc. All rights reserved 202521

21. Context-Driven Recommendations: Enriched
SIMILAR_TO
RECOMMEND_TO
SIMILAR_TO
PURCHASED_WITH
RECOMMEND_TO
cId: 12
cId: 12
Neo4j Inc. All rights reserved 202522

22. Customer Journey
Neo4j Inc. All rights reserved 202523

23. 24
Context-Driven Recommendations: Enriched
Neo4j Inc. All rights reserved 202524
Capture customer
interactions and customer
journey using a knowledge
graph
Analyze customer
interactions using
graph queries and find
customer communities
based on common
purchase behavior
Construct node
embeddings and
resolve entities based
on weighted pairwise
similarity between
various entities
Generate product
recommendations
based on
correlations
between products,
search queries and
historical purchases

24. Neo4j GraphRAG and GenAI
Interactive Session

25. LLM Projects - How are they going ?
Neo4j Inc. All rights reserved 202526
E
ff
e
c
t
i
v
e
n
e
s
s
Time
Poor domain knowledge
Hallucinations
Limited explainability
Security concerns
Siloed data
Model selection
Fine tuning
Parameter tweaking
RAG
Your expectations !
An API Key,
17 lines of code
in a few weeks
Amazing !!!!
…. and still
Your opportunity with
Knowledge
Graphs

26. Neo4j Inc. All rights reserved 202527
Do you remember this news?

27. LLMs make things up
Hallucination

28. Avoiding Hallucination
Prompt Engineering
Iteratively refining instructions to achieve
more consistent results.1
.
In-Context Learning
Provide examples to guide AI for accurate,
task-specific responses.
Also known as Few-Shot Prompting
2
.
Fine-tuning
Providing additional training to an LLM after
its primary training phase.3
.
29Neo4j Inc. All rights reserved 2025

29. 30Neo4j Inc. All rights reserved 2025
How can organizations use
domain-specific knowledge
to rapidly build accurate,
contextual, and explainable
GenAI applications?
The Big
Question

30. Retrieval-Augmented Generation Is Becoming
an Industry Standard
31Neo4j Inc. All rights reserved 2025
RAG augments LLMs by
retrieving up-to-date external
data to inform responses:
●Provide domain-specific,
relevant responses
●Reduce hallucinations with
verified data
●Enable traceability back
to sources

31. Use-case where naive vector search fails miserably
Chunking
Indexing Retrieving
Text chunks
Contracts
Vector index
Retrieved chunks
from different
contracts
Example question: Who manages our contracts with Neo4j?

32. Why RAG With Vector Databases Fall Short
1
3
2
4
Similarity is insufficient for rich enterprise reasoning
Only leverage a fraction of
your data: Beyond simple
“metadata”, vector databases
alone fail to capture relationships
from structured data
Miss critical context: Struggle to
capture connections across
nuanced facts, making it
challenging to answer multi-step,
domain-specific, questions
Semantic Similarity
≠ Relevance:
Vector search uses an incomplete
measure of similarity. Relying on it
solely can result in irrelevant and
duplicative results
Lack explainability:
The black-box nature of
vectors lacks transparency
and explainability
33Neo4j Inc. All rights reserved 2025

33. Why Knowledge Graphs ?
Neo4j Inc. All rights reserved 202534
Connecting
structured and
unstructured data
Structured Data
Unstructure
d Data
Extracted
Graph Data
Text
embeddings

34. Neo4j Inc. All rights reserved 202535
RAG with Neo4j
Find similar documents
and content
Identify entities
associated to content and
patterns
in connected data
Improve GenAI inferences
and insights. Discover new
relationships and
entities
Unify vector search, knowledge graph and data
science capabilities to improve RAG quality and
effectiveness
Vector Search
Graph
Data Science
Knowledge
Graph

35. #9307712
Q&A

36. Quick Break!

37. GraphRAG in Action

38. Personalisation Example Overview
●Real-world data from the Kaggle H&M Personalised Fashion
Recommendations Dataset
●Combines multiple structured datasets and unstructured data about
articles of clothing and customer purchases
●Leverages Neo4j’s Vector Index on nodes in the graph
Neo4j Inc. All rights reserved 202539

39. Demo Time!
Neo4j Inc. All rights reserved 202540

40. Semantic Search + Graph
Neo4j Inc. All rights reserved 202541
Vector Similarity
Search
Vector Similarity + Local
Graph Traversals
Vector Similarity +
GDS-Based Graph
Traversals
Find relevant documents and
content for user queries.
Find people, places, and
things associated to content.
Identify patterns in connected
data.
Further improve search
relevance using graph
algorithms and ML to
discover new relationships,
entities, and groups.
Vector Search
HNSW
Graph Database Graph Data Science

41. What Does “Similarity” Mean?
Neo4j Inc. All rights reserved 202542
It Depends:
●Text Embeddings => Semantic similarity, the meaning behind a text
sequence
●Graph Embeddings => Similarity in position or structure in a graph -
can have semantic meaning too

42. Step 1. Vector Similarity Search Only
Starts with an indexed vector
embedding on each node
Uses Neo4j as if it was a vector
database:
●Natural language search phrase
●Performs a vector similarity
search (i.e., cosine similarity)
●Return the top N results
●Each returned node is an
individual chunk of data
Neo4j Inc. All rights reserved 202543
Visualization of results from vector search-only approach

43. Neo4j Inc. All rights reserved 202544
Results: Vector Search Only
Product CodeProduct TypeDocument
Similarity
Score
842001 Sweater Product-- Name: Betsy Oversized || Type: Sweater… 0.945937
817392 Sweater Product-- Name: Japp oversize sweater || Type: Sweater… 0.944246
709418 Sweater Product-- Name: DIV Anni oversize hood || Type: Sweater… 0.932580
860833 Sweater Product-- Name: Runar sweater || Type: Sweater… 0.931568
893141 Sweater Product-- Name: Sandy || Type: Sweater… 0.930025
812167 Sweater Product-- Name: Macy || Type: Sweater… 0.929781
690623 Sweater Product-- Name: Simba || Type: Sweater… 0.928991
557247 Sweater Product-- Name: Petar Sweater(1) || Type: Sweater… 0.928751
538283 Sweater Product-- Name: TOR Sweater || Type: Sweater… 0.927127
687934 Sweater Product-- Name: Sister off shoulder || Type: Sweater… 0.927100
Search Term: “oversized sweater”

44. Step 2. Vector Similarity + Local Graph Traversal
Augments vector similarity search
with information already encoded in
the knowledge graph
●Start with a vector similarity
search
●Performs a local graph
traversal on each matching
node
●Return the additional context
that would not be available from
vector similarity search alone
Neo4j Inc. All rights reserved 202545
Visualization of results from
vector similarity + local traversal approach

45. Neo4j Inc. All rights reserved 202546
Semantic Search + Traversal
Purchases
in common
Customers
Target
Customer
Semantically
Similar
Products

46. Neo4j Inc. All rights reserved 202547
Only repeat result
Results: Vector Similarity + Local Traversal
Product
Code
Product
Type
Document
Search
Score
Purchase
Score
Vector-Only
Rank
677930 Sweater Product-- Name: Queen Sweater || Type: Sweater… 0.922999 6 NaN
516712 Top Product-- Name: Jess oversize LS || Type: Top… 0.922911 5 NaN
557247 Sweater Product-- Name: Petar Sweater(1) || Type: Sweater… 0.928751 4 7.0
675408 Sweater Product-- Name: Mother || Type: Sweater… 0.920846 4 NaN
669682 Sweater Product-- Name: Irma sweater || Type: Sweater… 0.921362 2 NaN
640755 Sweater Product-- Name: Allen Sweater || Type: Sweater… 0.926152 1 NaN
687948 Hoodie Product-- Name: Annie Oversized Hood || Type: Hoodie… 0.925855 1 NaN
709991 Sweater Product-- Name: SISTER OL || Type: Sweater… 0.924914 1 NaN
687856 Jacket Product-- Name: Jacket Oversize || Type: Jacket… 0.924428 1 NaN
674826 Sweater Product-- Name: Fine knit || Type: Sweater… 0.921296 1 NaN
kg_personalized_search.similarity_search(“oversized sweater”)

47. Step 3. Knowledge Graph Inference & ML
Neo4j Inc. All rights reserved 202548
Draw connections between highly
interconnected nodes and/or
those that have similar roles in
the graph
0.2
0.3
0.6
-0.6
0.1
0.4
0.5
-0.4
-0.1
0.5
0.4
-0.4

48. Neo4j Inc. All rights reserved 202549

49. Create a Co-purchase Projection
# graph projection - project co-purchase graph into analytics workspace
gds.run_cypher('''
MATCH (a1:Article)<-[:PURCHASED]-(:Customer)-[:PURCHASED]->(a2:Article)
WITH gds.graph.project("proj", a1, a2,
{sourceNodeLabels: labels(a1),
targetNodeLabels: labels(a2),
relationshipType: "COPURCHASE"}) AS g
RETURN g.graphName
''')
g = gds.graph.get("proj")
Neo4j Inc. All rights reserved 202550

50. Generate Graph Embeddings
# create FastRP node embeddings
gds.fastRP.mutate(g, mutateProperty='embedding', embeddingDimension=128,
randomSeed=7474, concurrency=4, iterationWeights=[0.0, 1.0, 1.0])
# Compute KNN and write relationships
knn_stats = gds.knn.write(g, nodeProperties=['embedding'],
nodeLabels=['Article'], writeRelationshipType='CUSTOMERS_ALSO_LIKE',
writeProperty='score', sampleRate=1.0, initialSampler='randomWalk',
concurrency=1, similarityCutoff=0.75, randomSeed=7474)
Neo4j Inc. All rights reserved 202551

51. Create a Recommender Graph
Neo4j Inc. All rights reserved 202552
MATCH (:Customer {customerId:$customerId})
-[:PURCHASED]->(:Article)
-[r:CUSTOMERS_ALSO_LIKE]->(:Article)
-[:VARIANT_OF]->(product)
RETURN
product.productCode AS productCode,
sum(r.score) AS recommenderScore
ORDER BY recommenderScore DESC
LIMIT $k

52. Product Code
Product
Type
Document
Recommender
Score
Vector-Only
Rank
562252 Trousers Product-- Name: Space 5 pkt tregging || Type: Trousers… 5.50 NaN
658030 Trousers Product-- Name: Push Up Jegging L.W || Type: Trousers… 3.68 NaN
607347 T-shirt Product-- Name: Beck L/S || Type: T-shirt… 3.68 NaN
863561 Bra Product-- Name: Alexis seamless top Rio Opt1 || Type: Bra… 2.78 NaN
647684 T-shirt Product-- Name: GABBE || Type: T-shirt… 1.89 NaN
860833 Sweater Product-- Name: Runar sweater || Type: Sweater… 1.86 4
657159 Flat shoe Product-- Name: OL ALFONS PQ Espadrille || Type: Flat shoe… 1.86 NaN
867240 Cardigan Product-- Name: OKLAHOMA OVERSHIRT || Type: Cardigan… 1.86 NaN
661417 Vest top Product-- Name: BAE top with inner bra || Type: Vest top… 1.85 NaN
674606 Skirt Product-- Name: CHARLIE SKIRT || Type: Skirt… 1.85 NaN
Neo4j Inc. All rights reserved 202553
Results: Vector Similarity + GDS Traversal
Search Term: “oversized sweater”
Only repeat result

53. Neo4j Inc. All rights reserved 2025
Langchain Chain
54
customer_id
searchPrompt
Personalized
search
Reco
timeOfYear
customerName
prompt llm personalize
d email
{searchProds: searchPrompt | personalizedSearch
(customer_id)
recProds: customer_id | recommendations
customerName
timeOfYear}
prompt | llm | OutputParser
s
e
a
r
c
h
P
r
o
d
s
recProds

54. Let’s take a look at the code !
Neo4j Inc. All rights reserved 2025
open genai-workshop.ipynb
https://github.com/neo4j-product-examples/genai-workshop
55

55. Neo4j Resources
Neo4j Inc. All rights reserved 202556

56. GenAI Ecosystem Integration
Neo4j Inc. All rights reserved 202557 dev.neo4j.com/genai

57. Graph Academy
What is Graph Academy?
Free, Self-Paced, Hands-on Online Training to help you learn how to build, optimize
and launch your Neo4j project, all from the Neo4j experts.
What’s more?
2 free certifications designed to test you on your overall knowledge of Neo4j:
●Neo4j Graph Data Science Certification
●Neo4j Certified Professional
Interested? For more information visit:
www.graphacademy.neo4j.com
58Neo4j Inc. All rights reserved 2025

58. Thank You!

59. Appendix
Neo4j Inc. All rights reserved 202560