Master Deck: GraphSummit Bengaluru (Oct 7)

TABLE OF CONTENTS
PLEASE DELETE BEFORE SHOWTIME
Housekeeping: 1 - 8
Keynote: 9 - 53
Product Vision 54 - 95
Add customer decks in at 95

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Agenda
09:30 Welcome from Neo4j
Ish Thukral, Country Head-India, Neo4j
09:40 Graphs + AI: Transform Your Data Into Knowledge
Jim Webber, Chief Scientist, Neo4j
10:10 Connected Data, Smarter AI: The AWS -Neo4j Advantage
Biswajit Das, Head of Data & AI, AWS
10:30 Graph-Driven Digital Twin of Enterprise IS: The Infosys Approach
Sunil Thakur, Associate Vice President–Data & Analytics at Infosys
10:50 Morning Break & Networking
11:10 How OpenText Leverages Neo4j Graphs to Modernize IAM
Shalabh Garg, Principal Product Manager, OpenText
11:30 Neo4j: Product Vision & Roadmap
Anurag Tandon, VP of Product Management
& User Experience Neo4j

Agenda
12:00 Q&A Panel
12:30 Lunch
14:00 Concurrent Hands-On Workshops
Option A: Create a Graph-Backed App from Scratch | Level 1, Junior Ballroom
Option B: Building Smarter GenAI Apps with Knowledge Graphs | Level 1, Grand Ballroom
16:30 Networking Drinks & Snacks

Graphs + AI:
Your Enterprise
Advantage
Dr. Jim Webber | Chief Scientist | Neo4j

PLACEHOLDER- FINAL SLIDE TO
BE PORTED IN WEDS Ready for design,

Graphs
create a
more
intuitive,
connected
view of
data.
Simple,
but
powerful.

The power of
the graph model
Relationships
connect nodes
:LOCATED_IN
Relationships can
have properties
(key-value pairs)
name: Lakshmi Kumar
date_of_birth: 1984-03-01
employee_ID: 1
City Company Employee
Nodes represent
entities
Relationships
are directional
:CEO_OF
start_date:
2022-11
toponym: Bengaluru
nickname: Garden City
org: Ajeya AI
type: Pvt. Ltd. Company
founded: 2018
Nodes typically
have properties too
Nodes usually have
one or more labels

Graphs naturally
and humanely
represent
complex
systems, cutting
through
complexity.

23
Trusted by 84 of the
9 / 10
Top Telcos
9 /10
Top Aerospace & Defense
20 / 20
Top US banks
10 / 10
Top Automakers
9 / 10
Top Pharmaceuticals
10 / 10
Top Technology & Software
8 / 10
Top Insurance Companies
8 / 10
Top Retailers

What is this?
● Has been extensively
trained on a wide-range of
eclectic subjects
● Answers conﬁdently, even
when making things up
● Opaque about reasoning
● Expensive
25

Knowledge
Facts
Context
Language
Statistics
Creativity
KG LLM
AI needs knowledge graphs
+

● LLMs are a lossy compression of the
internet as it was
● Knowledge graphs provide lossless
information you care about now

AI is changing how we develop applications.
Quickly.
"AI is fundamentally disrupting
software development ...”
-Innovation Endeavors (VC) / Axios, June 2025
“...AI pipelines need dynamic schemas
to [handle] evolving sources.”
-2025 Future Enterprise Resiliency & Spending, IDC
“A business data fabric reduces
latency and powers advanced AI…”
-Venture Beat, Enhancing business agility with rapid
data integration and advanced AI, 2025
"Organizations with rich, [connected]
knowledge graphs will have a
signiﬁcant competitive advantage…”
-Gartner Perception is the New Superpower For the
Future of Analytics and BI, 2025
"[GenAI]…will disrupt the very
software that enabled the last wave
of transformation."
-Harvard Business Review, May 2025

Why we build applications is
NOT changing
Problem to Solve
Business Process to Map
Questions to Answer
Where
When
How
Who
What
?

Dynamic,
Context-Aware &
Autonomous
Applications
Predeﬁned
Workflows on
Simple CRUD
Data Models
Real time
decisions
Proactive
Connected
context
Agents working in
coordination
Behavior by code,
prompts & models
Agentic AI
Create
(Create, Read,
Update, Delete)
Upload
Read
Update
Delete
CRUD

From Static Logic to Dynamic Reasoning
1
Hardcoded
rules deﬁned at
compile-time
Create
(Create, Read,
Update, Delete)
Upload
Read
Update
Delete
CRUD
Reason, adapt & make
decisions in real time
based on goals, context
& changing inputs
Real time
decisions
Proactive
Connected
context
Agents working in
coordination
Behavior by code,
prompts & models
Agentic AI

From Request-Response to Goal-Oriented
2
React to user
input
Create
(Create, Read,
Update, Delete)
Upload
Read
Update
Delete
CRUD
Proactively pursue goals,
orchestrate tasks, and
learn from feedback
Real time
decisions
Proactive
Connected
context
Agents working in
coordination
Behavior by code,
prompts & models
Agentic AI

From Monoliths to Composable Systems
3
Tightly coupled
architecture
Create
(Create, Read,
Update, Delete)
Upload
Read
Update
Delete
CRUD
Multiple specialized
agents working in
coordination
Real time
decisions
Proactive
Connected
context
Agents working in
coordination
Behavior by code,
prompts & models
Agentic AI

From Code-Centric to Prompt-Driven
4
Behavior deﬁned
exclusively by
code
Create
(Create, Read,
Update, Delete)
Upload
Read
Update
Delete
CRUD
Behavior deﬁned by a
combination of code,
prompts and AI models
Real time
decisions
Proactive
Connected
context
Agents working in
coordination
Behavior by code,
prompts & models
Agentic AI

From CRUD to Context
5
Logic centered
around
Create Read
Update Delete
Create
(Create, Read,
Update, Delete)
Upload
Read
Update
Delete
CRUD
Logic set by reasoning,
goals & rich, connected
context (users, tasks,
history, environment)
Real time
decisions
Proactive
Connected
context
Agents working in
coordination
Behavior by code,
prompts & models
Agentic AI

1 From Static Logic to Dynamic Reasoning
From Request-Response to Goal-Oriented
2
From Monoliths to Composable Systems
3
From Code-Centric to Prompt-Driven
4
From CRUD to Context
5
The Application Architecture is Changing

Most data is still stored
in rows & columns
Structured for ﬁxed
questions and static data
Relationships are implied,
or even lost
Apps will
change.
So must the
data layer.

What must
a data layer
for AI do?

Kiruna Table
Chat History Hi, I'm ABK the carpenter and I ﬁxed this problem by applying 2 drops of
glue to the table legs before step 8.
Product
Bill-of-materials
Product Issue
Tracker
Kiruna Table
Assembly Guides

Kiruna Table
Chat History Hi, I'm ABK the carpenter and I ﬁxed this problem by applying 2 drops of
glue to the table legs before step 8.
Product
Bill-of-materials
Product Issue
Tracker
Kiruna Table
Assembly Guides
Uniﬁed
RAG corpus
and agentic
memory

Properties of an amazing knowledge layer for AI
Efﬁcient Storage of
Structured, Semi-structured
and Unstructured data
1
Connects Enterprise Data
Within and Across Silos to
Provide Context for AI
2
Stores RAG Corpus and
Agentic Memory in a Uniﬁed
Platform
3
Enterprise Strength
Governance & Explainability
for trust, transparency and
compliance
4

Powerful, but it
doesn’t support
relationships
And relationships
are crucial for
context
Data Platforms
Apps Agents Tools
Application Layer
Shopping Cart Fraud Catalog Sales
But what about my existing data platform?

Data
Platforms
Apps Agents
Knowledge Graph Layer
Tools
Data Source
Data Source
Data Source
The next layer in the AI stack:

We have learned building RAG apps that
you need a more sophisticated retrieval
system. It's not good enough to have
just vector search and some
embeddings.” Satya Nadella @
Microsoft BUILD 2025 keynote
“Models are just part of the equation. For
anyone building an agent you need to be
able to have truly great access to the real
time web as well as the entire enterprise
knowledge graph.
47

Dr. Swami Sivasubramanian (AWS reInvent 2024 -Keynote)
GraphRAG-Improves Accuracy & Explainability
48

build the knowledge graph
layer for your AI applications
to enable you to

Free Aura
Credits
Free Help From
Graph Experts
Co-Marketing
Support

What does this
look like in
52

53
So what do people do?
Leading video game co.
● 150+ non-technical analysts need real-time access to
data using natural language
● No holistic view across silos
● Get answers to complex questions, e.g. “How do
competitor launches impact our sales?”
CHALLENGE
Reduction in analyst time spent on routine requests.
Time-to-insight vs. traditional analytics. Time for analysis
reduced from weeks to seconds

54
How? A knowledge layer architecture

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

Relationships are everywhere
To power your intelligent applications & systems
They transform your data into knowledge

Enjoy Graph Summit
Dr. Jim Webber | Chief Scientist | Neo4j

Digital Twin for
IT systems
Sunil Thakur
AVP (AI & Data Infosys IT)
Infosys

Since 2019
Infosys Knowledge Graph
Journey with Neo4j
Learning
Recommendations
2020
2024 - now
IS Graph
hAIreNxt
2020 and early 2021
Talent/Hiring
Recommendations
Publish Content
Recommendations
2022
2022
Account Marketing
Recommendations
Skill and other
ontologies
2023 - 2024
19 Use cases 15 m Nodes 191 m Relationships

Dependencies
Are you too dependent on specific individuals to
manage your code?
Do you lack clear visibility into application
dependencies, lineage, and code explainability?
Are your technical documents outdated?
Hidden cost & complexity of legacy systems
Why is this application down?
Who are the impacted users?
Who is the owner of this code / server / process / …
What does this piece of code do?
If I make this change, what else will get impacted?
How does this process work

IS Graph
Digital Twin for IT Systems
Centralized lineage solution built to know cross application
code, data, process, Infrastructure and human
dependencies – how are they interconnected, impact of one
to another & quick analysis

Discovery & Parsing (Gen AI)
Legacy Landscape Code,
System data, Documents
IS Graph
Automation of Information extraction, processing and
inference
Modeling & integration (Neo4j)
Network schema of Nodes & Edges
Inference & Action (Gen AI)
Agentic AI, RAG and Natural
Language Querying

Impact
Analysis
Use graph to get
impact application
and objects for any
SP, SSIS, Cube, K8
Service and many
more
Key Capabilities
Monitoring
& Alerts
Monitor the uptime
of all system and
infrastructure
components
GenAI
RCA
Automated error
identification and
analysis
Gen AI
Chatbot
Use Gen AI to
query about
domain, process
and system
implementation
Access
Validations
Monitor the uptime
of all system and
infrastructure
components
S/PII
Data Lineage,
archival and
GDPR
compliance
IS Graph

Productivity & Efficiency
40% effort saving for
developers in impacted role
Code parsing & explainability –
quickly know the impact of change
in your code cross application,
understand your code
Inspirational
Infosys inhouse solution
with capability to visualize and
analyze the impact across the
organization using Neo4j
Targeted reach,
Quick Turn around
Helps to effectively collaborate
via identified impacted
application, SPOCs and not
spam each other.
What are we achieving?

Key Takeaways
Power of Knowledge Graph + Generative AI
Complexity is now navigable
Treat your entire IT environment as a single unified graph
Gen AI is your Data Translator
Gen AI efficiently parses and interprets unstructured data,
including code, configuration files, and system documents.
Neo4j Graph is the intelligent brain
Graph integrating those facts to form a digital twin, which can
answer multi hop complex questions
Gen AI links interdependencies in real time
Gen AI enables interacting with the Digital Twin using natural
language querying

Sunil Thakur
AVP (AI & Data Infosys IT)
Infosys
Thank You
Sunil_Thakur@infosys.com
Radhika_Jampani@infosys.com

Modern IAM with
Neo4j
Shalabh Kumar Garg
Principal Product Manager
07/10/2025

73
OpenText ©2025. All rights reserved. 73
OpenText ©2025. All rights reserved.
OpenText at a glance
22,000
employees
99
of top 100 global companies
are customers
180
countries where we
serve customers
31M
public cloud users
9,000
private cloud
deployments
120K+
enterprise customers

74
OpenText ©2025. All rights reserved. 74
Business AI
OpenText Cloud Platform
OpenText Services
• Cloud delivery platform across all OpenText cloud products
• Automated deployment of services into different environments
• Unified cloud platform for cloud applications, integrations, microservices
• Single identity, simplified administration, streamlined operations
• Advisory and Implementation Services
• Managed Services and Learning
• Advanced Customer Support
• Customer Success Services
Multi-Cloud Ecosystem
OpenText Data Cloud
• Content Management and Metadata Management APIs
• Communications and Business Network APIs
• Secure Data Management APIs
• Threat Intelligence APIs
Trading Partners | Connected Subscribers | Private Cloud Environments
Strategic Partners | System Integrators | Distributors | Resellers | MSPs
Business Clouds
Content
Document
Management
Capture & IDP
Information
Archiving
Process
Automation
Business
Integrations
Industry &
Departmental
Applications
Business Network
Industry
Applications &
Services
B2B
Integration
Supply Chain
Orchestration
Supply Chain
Insights
Supply Chain
Traceability
Secure
Collaboration
Experience
Customer
Communication
s
Omnichannel
Messaging
Web & Mobile
Experiences
Digital Asset
Management
Customer
Journey & Data
Digital Fax
Cybersecurity
Antivirus and
Identity
protection
IAM &
Application
Security Testing
Backup and
Recovery
Security
Operations
Data Security
and Protection
Cyber Risk
Management &
Resilience
DevOps
DevOps
Platform
Quality
Management
PPM and
Strategic
Portfolio
Management
Functional
Testing
Performance
Engineering
Test & Release
Automation
Observability &
Service Management
Service
Management
Infrastructure &
Application
Automation
Universal
Discovery
Observability
and AIOps
Network
Management
FinOps &
GreenOps
Analytics
Enterprise
Data
Warehouse
Data
Lakehouse
Data
Ingestion &
Transformation
eDiscovery
with AI
Business
Intelligence &
Insights
AI &
Advanced
Analytics

75
Worldwide Leadership across Industries
20 out of top 20
Manufacturing Companies
20 out of top 20
Telecom Companies
20 out of top 20
Insurance Companies
19 out of top 20
Retail Companies
20 out of top 20
High Tech Companies
20 out of top 20
Life Sciences Companies
20 out of top 20
Healthcare Companies
20 out of top 20
Automotive Companies
20 out of top 20
Federal Governments
19 out of top 20
Transportation Companies
20 out of top 20
Banking Companies
20 out of top 20
Oil & Gas Companies
Sources: Forbes Global 1000 and SAP Enterprise Customer List (provided by Rev Ops)
Assessment updated by Competitive and Market Intelligence June 2025
20 out of top 20
Utilities Companies
20 out of top 20
Consumer Packaged Goods Companies
99
of the 100 largest companies in
these key industries are
OpenText Customers

76
IAM Portfolio

77
OpenText IAM Portfolio – Complete Core Capabilities
Leveraging Identity to provide secure access, effective governance, scalable automation, actionable analysis and intelligent insight
• Access requests
• Access governance and certification
• Access workflows
• Security orchestration
• Business and technical role
management
• Integration to directory services
• API driven capabilities
• Windows Policy Management
• Access management to
unstructured data
• Authorization
• Monitoring, audit, compliance
• Complete list of authenticators
• MFA and passwordless options
• Integrated Risk service
• Adaptive/continuous
• IOT support and SDKs
• Mobile apps
• Integration and synchronization with
directory services
• Federated provisioning (JIT)
• Centralized management of users,
authorization policies, etc.
• User self-service (access, pwd, etc)
• Password mgmt
• User onboarding and registration
• Audit, compliance, reporting
• Keystroke and video recording
• Secrets/credential vault
• API driven
• JIT provisioning
• Authorization Services
• Reverse/forward proxy, API security
• Session management
• Full federation, OIDC, OAuth support
• Support for legacy apps
• ITSM integration
Access
Management
Privileged
Access
Management
Administrative
Tools
Privacy and
Consent
Adaptive
Authentication
Identity
Lifecycle
Management
Identity
Governance
Data Access
Governance
Access
Intelligence
and analytics

78
OpenText ©2023 All rights reserved 78
OpenText IAM holistic portfolio
Identity Foundation & Shared Services
Identities Resources History Analytics Cloud Bridge Risk
Self Service
Privileged Access
Management
Workforce Identity
B2B/B2E
Customer Identity
B2C/G2C
Identity Governance
& Administration
Access
Management Policy Orchestration

79
Application
Application
Advanced
Authentication
Service
Access Management
Authorization Service Application
Policy
Enforcement
Point
(PEP)
Policy Information
Point (PIP)
Policy Decision
Point (PDP)
or
Application Data
Application
Entitlements
Identity Governance
Collecting
Entitlements
Provisioning
Entitlements &
Assignments
or Internal
PDP
Internal
PEP
JIT External
Attributes
Policy
Administration
Point
(PAP)
User
Authenticates
and attempts
access to
application
Entitlements
Decision Maker
reviews access
MFA
Risk Engine
Risk Level
Identity Life Cycle Management and Change Orchestration
Step up
Authentication
App Identities and
their Attributes
Proprietary Authorization Mechanism
Join / Move / Leave
Processing
Identity Correlation
Attribute-level
Authority
Event
Transformation
Detecting
change
events
Submitting
change
events
Common Identity
Repository (Neo4j)
Privileged Session Management
Credential Vault

80
Database Choices

81
Database Choices
Object Database
Key Value Pair Database
• DynamoDB
• FoundationDB
Columnar Database
• Cassandra
• Vertica
• ScyllaDB
Relational Database
• PostgreSQL
Graph Database

82
• Representing relationships and prop
• Traversal queries are highly perform
• Flexible and extensible data model
• Direct fit for complex identity data
• Properties on edges as well
• Single graph for one tenant
• Options considered
• Memgraph
• Janus Graph
• Neo4j
Why Graph

83
Why Neo4j
• Easy to learn query language
• ACID compliant
• Transaction support
• Active graph community
• Cloud service presence
• Multiple cloud support
• Native graph storage and processing
• Reliable and scalable
• CDC Module

84
Query Performance

85
Current State
Identity Graph
Identity Relations
Application Associations
Identity Change Event System
Group Permissions
RBAC

86
Future
• Entitlement Graph
• Current State Snapshot
• Analytics Integration
• Combined Identity and Entitlement g
• Threat detection

opentext.com · twitter.com/opentext · linkedin.com/company/opentext

Anurag Tandon
Product Vision &
Roadmap
VP Product Management, User Experiences
anurag.tandon@neo4j.com

Your Business
is a Graph
Employees
Network & Security
Suppliers
Product
Customers
Finance
Process

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
Finance
Process
Your Business
is a Graph Customers

Setting the Pace
Open Source
Release
2007
Cypher Query
Language Launch
2011
First Production-Ready
Deployment
2009
Graph Data Science
and MultiDB
2020
Neo4j AuraDB
Launch & Neo4j
Fabric
2019
ISO Announces
GQL Standard
2024
Native Vector Search
Capabilities in Neo4j
2023
Browser and Labels
2013
OpenCypher Project
Launched
2015
Distributed Graph
Database (Clustering)
2017

Technology Trends
Driving Innovation

For developers, data analysts, and data scientists
Premium, trusted cloud-native graph intelligence platform
Cross cloud, easy to use, and enable AI accuracy

Most powerful
database for
graphRAG
Most
developer-friendly
platform for
agentic AI
Deep integrations
across the
AI ecosystem

Provides foundational services
like memory to power all
AI platforms.
Agentic Brain
Graph Intelligence Platform
Browser (Query) Bloom (Explore) NeoDash(Dashboards) Data & Document Import GraphQL
Aura Agents
Build multi hop agents
in seconds.
Graph Engine
Graph
AI
AI
Powered
Graph
Tools
Database
& Graph
Algorithms

Fully Managed
Seconds to Sign Up
Minutes to Wow
Days to Value Integrated Ecosystem
Strategic Investments
Trusted Fundamentals Scalability with Enterprise Security, Governance, and Compliance
c

Fully Managed
Seconds to Sign Up
Minutes to Wow
c

Autonomous
Clustering
High availability with multi-DB support.
Servers and databases are decoupled:
servers provide computation and storage
power for databases to use. Each
database relies on its own autonomous
cluster, organized in primaries and
secondaries.
Scalability, allocation / reallocation,
service elasticity, load balancing,
automatic routing are automatically
provided (or they can be ﬁnely controlled).
3 primary
3 primary +
2 secondary
Composite
Database
APP APP APP
Applications
Databases
Servers
CORE DATABASE ENHANCEMENTS

Graph that ﬁts on single
machine in a single clustered
environment replicated for
read scalability.
Replicated Graph Federated Graph
(Fabric)
C
Different graphs (supply chain
& parts graph) in an
organization being queried to
aggregate information.
Could be Replicated
or Sharded.

INTRODUCING
➔ 100TB+ scale with full ACID compliance
➔ One system for transactions and analytics
➔ Distributed by design, full graph integrity
➔ AI-ready -billions of vectors directly in the graph

Create Large Scale Sharded Graphs
INFINITE SCALABILITY
C
Sharded Graph
(InﬁniGraph)
Single Graph sharded across
many machines across
clustered environments.
Growing need to support
large graph sizes.
Graph that ﬁts on single
machine in a single clustered
environment replicated for
read scalability.
Replicated Graph Federated Graph
(Fabric)
C
Different graphs (supply chain
& parts graph) in an organiz-
ation being queried to
aggregate information.
Could be Replicated
or Sharded.

Large Graph
Support
● High data volumes-up to 100TB (and
potentially more) of graph data
● Fast data loading-initial bulk import and
staging/live incremental updates
● Semantic indexes-sharded full-text and
vector indexes are natively integrated
● ACID-fully transactional and ACID compliant
● Simplicity-Transparent to user, standard
Cypher
● Analytics-supports Neo4j tools (Query,
Explore, Graph Analytics)
● Transparent to all API calls
NEW CAPABILITIES
EAP Available
GA-Planned Q4 2025
Demo
</>
X
Streaming
Operational
Analytical
READS
Reporting
T
Autonomous Clustering
Property Shards
TXN logs
Graph Shard
101
01
101
01
101
01
101
01
101
01
WRITES
Bulk WRITES
Bulk WRITES

Centralized Management
Monitor & manage all Neo4j databases across the
enterprise
Proactive Monitoring
Insights and health monitoring to optimize
performance and reliability
Identify security risks
Streamlined Operations
Seamless workload operations across databases
to reduce risk and downtime of your enterprise
data
Easily migrate self managed databases
to Aura with few clicks
Fleet
Management
HYBRID ADMIN EXPERIENCE
Aura (Uniﬁed Fleet Management)
Aura Self Managed
ENTERPRISE DESKTOP COMMUNITY
Now Available

Block Format is superior graph native
format to group graph data into blocks
that reduces amount of data read with
greater performance in
memory-constrained scenarios
Parallel Runtime speeds up analytical
queries up to 100x by concurrent
execution on all cores
CDC enables graph changes published as
events in real-time (full or diff mode)
Autonomous clustering with multi-db for
multi-tenant SaaS solutions
Performance &
Scalability
Block Format Parallel Runtime
Change Data Capture Autonomous Clustering (with Multi DB)
Now Available

GQL as ISO Standard for Graph Queries
Conditional Queries to provide more
flexibility and expressive power to handle
complex querying scenarios
Quantified graph patterns & Quantified
path patterns for simplified execution
Cypher version can be selected at DBMS,
database and single-query level: server
upgrades do not require query migration.
Query Language is decoupled from the
server releases
Language
Enhancements
MATCH (n:Person)
CALL (n) {
WHEN n.age > 60 THEN {
SET n.ageGroup = 'Veteran'
}
WHEN n.age >= 35 AND n.age <= 59 THEN {
SET n.ageGroup = 'Senior'
}
ELSE {
SET n.ageGroup = 'Junior'
}
}
RETURN n.name AS name, n.ageGroup
Conditional Queries Graph Pattern Matching
Quantified Path Patterns Cypher Versioning
Now Available

Security &
Operations
Graph Schema Property Based Access Control (PBAC)
Ofﬂine Incremental Importer Differential Backup & Point in Time Recovery
Now Available
Schema: Unique relationship property,
relationship key, property data types
Property Based Access Control (PBAC)
for RBAC, data-driven rules to control
READ and TRAVERSE privileges on
nodes
Ultra-high speed incremental importer
addition to ofﬂine importer
Incremental backup and recovery

● Replication of data from one
database instance to another.
● Replication across instances
running in any conﬁguration
(across DCs, Clouds platform,
Self managed vs Aura).
● Neo4j to be Tier 1 platform in
enterprises with potential to
deliver RPO (0) /RTO (15min)
Cross-Cluster
Replication
CORE DATABASE Roadmap
Roadmap -2026
High Availability across DCs/Regions
Migrate Self Managed Databases to Cloud

Fully Managed
GraphRAG
AI Accuracy
Ease of Use
5Seconds to Sign Up
Minutes to Wow

Focus on Your App,
Not Infrastructure!
Available on Google Cloud, AWS, and Azure
99.95% uptime SLA with self-healing
cluster architecture
Scale up for large memory-intensive graphs
Automated backups and restore
Automated upgrades, zero maintenance
CLOUD FIRST CAPABILITIES
More than
30k
Managed
Databases
Available on all major
cloud providers

Aura Launches
2024/2025
Feature: Customer Managed Keys
(AWS)
GA Date: 18 April
Feature: GDS Sessions (w/console
UX) EAP Date: 6 Jun
Feature: Customer Metrics
Integrations
GA Date: 8 June
Feature: Security Log Forwarding
GA Date: 2 July
Feature: User Management Pro PLG-
GA Date: 3 July
Feature: 512GB RAM (AWS)
GA Date: 8 July
(Azure)
GA Date: 12 July
Feature: Query Log Analyzer
EAP Date: 22 July
(GCP)
GA Date: 31 July
Feature: Business Critical Tier v1
Date: 1st August
Feature: 512GB RAM GCP
GA Date: 13th August
Feature: SSO Conﬁg (Admin UI)
GA Date: 20 August
Feature: Query Log Analyzer
Date: 27 August
Feature: Pro Trial
Date: 2 September
Feature: UPX-Preview
Date: 2 September
Feature: Change Data Capture
Date: 4 September
Feature: VDC Name Change
Date: 4 September
Feature: Query API
GA Date: 6 September
Feature: Migration
Readiness Reports
EAP Date: 6 September
Feature: Console Consumption Reports
GA Date: 16 September
Feature: Mark DB as Production
Date: 14 October
Feature: Secondaries
GA Date: 21 October
Feature: Migration Readiness Report
GA Date: 7 November
Feature: 512Gi GCP Business Critical
Date: 15 November
Feature: Business Critical All Aura Regions
Date: 19 November
Feature: UPX GA Self Serve
Date: 11 December
Feature: 512GB Azure BC & VDC
GA Date: 12 December
Feature: Security Log Analyzer
Feature: Adaptive Email MFA
Feature: AuraDB Vector Optimised
Feature: India Region Pro & BC ‘all
clouds’
Date: 7 Jan
Feature: AuraDB Pro <128GB
(pre-paid)
Date: 13 Jan
Feature: Aura CLI
Date: 14 Jan
Feature: GraphQL for AuraDB
(BETA) Date: 14 Jan
Feature: AuraDB Pro Trial 14 day
length + all CSP Regions
Date: 23 Jan
Feature: AuraDB Latest version
(CalVer)
Date: 29 Jan
Feature: AuraDB: Graph Analytics
(Professional)
Date: 13 Feb
Q2 ‘24 Q1 ‘25
Q3 ‘24 Q4 ‘24

Comprehensive Cloud Offerings
for Your Workloads
Zonal (single AZ) with
functionality aimed at
smaller teams &
departmental solutions
Regional (Multi-AZ) with
enterprise-grade SLA &
functionality
Highest tier, regional
(Multi-AZ) with dedicated
account provisioned per
customer
Perpetually free
database for customers
to learn and experiment
with apps
Now Available

3 paid SKUs: Fit your workloads across Aura
Professional, Business Critical, and Virtual
Dedicated Cloud
Free Trial: Try Aura Professional before you buy
Scale Out: Secondaries provide ability to scale
out for read heavy workloads
Security: SSO using IDPs such as AAD and Okta,
encryption in-transit and at-rest; CMEK, private
VPC and private links, standard industry
compliance
New
Capabilities
Aura Pro (Free Trials)
Secondaries For Scale Out
AURA ENHANCEMENTS
Now available

Security &
Compliance
Trust Center
SSO using IDPs such as AAD and Okta
Fine-grained access control
Encryption in-transit and at-rest;
Customer Managed Keys (AWS)
Private VPC and Private Links
Standard Industry Compliance
AURADB NEW CAPABILITIES
Now available
114

IP Filtering
Control network access with IP allow-lists
on VDC and BC tiers
Works with public, private, or hybrid
network setups
No endpoints or DNS required, simple UI
or API setup
Enforce consistent network access rules
across instances and projects
AURADB NEW CAPABILITIES
Now available
115

Pay-as-you-go, Serverless offering providing
65+ built-in graph algorithms for use with any enterprise
data on any cloud platform
New
Graph Analytics
Use Graph Analytics in any data in any system without
full replication.
Deliver most advanced graph algorithms in industry across
CPU & GPU
Deliver serverless experience so customers only pay
for what they use
Enable real-time execution with high throughput scenarios
Enable interactive experiences with multiple analysts and
data scientists running parallel algorithms
GRAPH ANALYTICS AS KEY DIFFERENTIATOR
Serverless
Analytics
Higher Scalability & Concurrency
All Databases
Projection
Write Back
Now available

Fully Managed
GraphRAG
Cloud First AI Accuracy
Seconds to Sign up
Minutes to Wow
Days to Value
Zero Ops
Integrated Ecosystem

Uniﬁed Data Management and Visualization
Demo

Improved workflows with a single hub for
all data management tasks
Consistent user experience through a
single unified console across Neo4j tools
Easier collaboration as teams can share
resources and collaborate on projects
Improved productivity with GenAI copilot
by helping developers write and improve
Cypher queries
Secure data access with expanded roles
and new access controls
Unified
Experience
Data Import (growing sources) One Click Graph Data Modeling
AURA ENHANCEMENTS
Co-pilot for Cypher Adoption AI Powered Dashboards
Now available

VS Code enables syntax highlighting, auto
completions, connection mgmt, and more
JDBC Driver enables tool integrations,
supports SQL translation, and schema
mapping
Neo4j GraphQL library and service to
deploy low code API with GraphQL
Query API (Cypher over HTTPS) allows for
single Cypher requests with the response
returned as JSON
Model Context Protocol (MCP) Server
Developer
Surfaces
VS Code Extension Drivers & Connectors
DEVELOPER EXPERIENCE
GraphQL Support Query API
Now available

Unified local console with Neo4j
Enterprise Edition installation
Convenience of offline working
Enable remote connections, including
to Aura Databases
Unified login with Aura
Migrate local databases to Aura
Desktop V2
HYBRID DEVELOPER EXPERIENCE
Coming Soon
Coming Soon
Now available

Fully Managed
Cloud First Ease of Use
5Seconds to Sign Up
Minutes to Wow
Days to Value
Zero Ops
Integrated Ecosystem

GenAI
Language
Statistics
Creativity
KGs
Knowledge
Facts
Context
Knowledge Graphs Unlock GenAI
Accurate
Contextual
Explainable

GraphRAG is RAG where the R path
includes a knowledge graph.
What is GraphRAG?

Query
Response
User
Users
Graph
Retrieval Agent
Agents
Vector + Graph
Retriever
HRIS
Retriever
Tools
Text2Cypher
Retriever
SME Cypher
Template
LLM
(Planning)
LLM
(Output)
Graph
Database
1
5
4
2
3a
3b
3n
Agentic GraphRAG
HRIS
3
MCP

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
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
Higher Accuracy
1

127
Opaque & Implicit
Customer: "I actually already
ﬁxed a couple of bugs thanks
to this!”
Transparent & Explainable
2 Easier Development

X
Customer
Service
Doctor
Social Security
Number
Social Security
Number
Patient
Bob
Phone
Number
Health
Diagnosis
Improved Governance & Explainability
3

Higher
Accuracy
Easier
Development
Improved
Explainability
1 2 3

Vector
Support
RELEASED CAPABILITIES
Vector Index built into the database
Store any property, node, and relationship
as vector in the database
Ability to create embeddings by directly calling
various embedding services like OpenAI, Azure
OpenAI, VertexAI, and Bedrock
Vector Type, Distance Function, Pre-Filtering,
Scaling, external vector stores
Coming Soon

Construct knowledge graphs from unstructured
data/documents using schema
Implement different GraphRAG retrievers
Build GenAI RAG pipelines with vector and hybrid
search and GraphRAG retrieval
External vector search integration
GraphRAG
Python Package

Integrated with GenAI ecosystem
Orchestration & Agent Libraries
Support for major frameworks like
Langchain, LlamaIndex, Spring AI,
Langchain4j, Haystack, Semantic Kernel,
etc.
Integrated with all LLM platforms like AWS,
GCP, Azure, and OpenAI
Model Context Protocol (MCP), CrewAI,
Pydantic.AI, other Agent SDKs
GenAI
Ecosystem
● Graph Connector
● CypherQAChain
● KG Construction
● Vector Index
Integration
● Multiple LangChain
Templates
● LangChain.js
● LangChain4j
● Cypher Data
Loader
● Vector Search
Integration
● KG Construction
● Create Vector
Index
● KG Construction
● Query Vector
Index
● Embedding
Retriever
● Dynamic
Document
Retrieve (Cypher)
Coming Soon

MCP is the AI native tool integration
protocol for agents.
Expose Neo4j data to any agentic AI
system, enabling intelligent reasoning
that can break down tasks for
explainable, multi-hop retrieval on
knowledge graphs with an
ofﬁcially-supported MCP server
Announcing ofﬁcial MCP Server in Beta;
supports self managed and Aura Data
MCP Roadmap
● Neo4j Database & GDS (GA-Q4)
● MCP for Aura Agents (GA-Q4)
● Aura hosted MCP Server (2026)
● MCP for Aura Management (2026)
MCP Servers
133
Beta-Available Now
NEW CAPABILITIES
github.com/neo4j/mcp

An agentic, API-driven service enabling
developers to build GenAI applications
in minutes
● Developer-Centric Agent API
provisioned at Database Level
● Low code Agent and Tool creation
with Cypher
● Simple UI to test chat experience
and potentially embed in apps
● Seamlessly integrate explainable,
multi-hop retrieval & reasoning into
your AI workﬂows
Aura Agent
Beta-Available Now
NEW CAPABILITIES

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