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The race for enterprise AI dominance has officially shifted from basic model experimentation to massive, production-grade deployment. As organizations scramble to ground their generative AI applications and complex reasoning engines in real-world logic, they face a critical infrastructure challenge: the traditional methods of moving, copying, and siloing data are no longer fast or cost-effective enough to keep up with enterprise demands.
In Q2, we focused on breaking down these infrastructure hurdles. By bringing Neo4j’s native graph intelligence directly to where cloud data already sits, we’re unlocking higher-performance delivery opportunities and unprecedented deployment velocity across our global ecosystem. From architectural innovations to strategic security expansions, this blog highlights how our latest milestones enable enterprise environments to scale value and accelerate data strategies in the second half of 2026.
Neo4j Launches Virtual Graph, Bringing Native Cloud Data Reasoning to Databricks and Snowflake
The launch of Neo4j Virtual Graph brings advanced graph intelligence straight to where enterprise data already lives. Natively integrated within the Databricks and Snowflake ecosystems, Virtual Graph provides organizations with a zero-copy, zero-ETL entry point to leverage existing cloud infrastructures without requiring costly data migrations.
Virtual Graph lets you run Cypher queries and graph algorithms directly against data in Snowflake, Databricks, and other lakehouses. Our zero-copy architecture ensures your data stays put, governed by existing controls, with no new system of record to manage—all while unlocking the power of Neo4j’s AI-powered Graph Tools.
By exposing the hidden relationships implied by your tables, Virtual Graph immediately prepares your data for graph analytics and the AI agents that need to reason over it. This breakthrough dramatically accelerates time-to-value for generative AI applications while maximizing the return on existing cloud data investments.
Acquisition of GraphAware Boosts Security Portfolio and SI Enablement
Complex enterprise threats, including sophisticated financial fraud networks and nation-state cyber risks, require deep visibility across heavily connected data environments. Neo4j’s strategic acquisition of GraphAware directly addresses this operational bottleneck by integrating an advanced investigator user interface natively onto our core enterprise data stack.
For global organizations, SIs, and strategic consultants, this acquisition delivers a comprehensive, highly competitive front-end solution to capture complex, high-stakes security workloads. The combined architecture allows security teams to rapidly model cyber assets, map compliance-as-code, and explore deeply nested, multi-tier transactional paths in real-time. By bridging the gap between deep graph analytics and intuitive front-end visual discovery, teams can now deploy highly secure investigative environments that instantly expose hidden threat patterns across fraud detection, corporate risk compliance, and national security domains.
“Export to Lakehouse” Capability Unlocks Repeated Pipeline Workloads with Microsoft Fabric
Neo4j has expanded its Microsoft Fabric integration, introducing a new native “Export to Lakehouse” capability that enables graph-enriched insights, calculated node properties, and complex structural relationships to sync directly back into Microsoft OneLake tables.
This update provides systems architects and engineering practices with a standardized, repeatable blueprint to unify disparate data streams. Instead of wrestling with custom integration code or fragile secondary data pipelines, teams can run high-performance graph algorithms in Neo4j and immediately write multi-hop relationship metrics back to unified Delta Lake formats.
This allows downstream native Microsoft BI tools, Synapse analytics, and Microsoft Copilot deployments to consume graph-intelligent feature stores natively, driving deeper optimization across the entire enterprise data architecture.
Neo4j Expands Partner Revenue Potential with Native Integration Inside Gemini Enterprise
Neo4j has achieved several key technical milestones on Google Cloud, delivering seamless, enterprise-grade bidirectional data synchronization and out-of-the-box integrations with tools like Google Gemini and Vertex AI (now called Gemini Enterprise Agent Platform).
These updates provide organizational field teams with a compelling architecture for modern data applications. Enterprise architectures can now achieve smoother, real-time agentic workflows in which Large Language Models (LLMs) can dynamically read from and write back to the graph, creating self-correcting memory loops.
Backed by expanded regulatory compliance and automated governance mapping, these native engineering touchpoints make it easier than ever to attach advanced graph capabilities directly to existing Google Cloud footprints, ensuring secure, multi-cloud AI sovereignty without sacrificing transactional performance.
Deploying Connected Graph Intelligence Across Databricks Platform Architectures
To help technical and business teams bridge the gap between isolated infrastructure and corporate strategy, Neo4j has delivered four specialized reference architectures natively optimized for Databricks environments.
Across complex, multi-layered industries, deploying graph structures alongside lakehouse architectures serves as a powerful translation layer. By grounding analytical layers with deep relationship context, organizations can successfully bypass traditional data fragmentation to solve high-value, systemic business problems.
These four concrete, repeatable blueprints turn raw cloud data investments into live, action-oriented intelligence engines:
: Diagnose systemic vulnerabilities across complex supplier networks, demonstrating that global supply chains do not fail at individual nodes but at points of connection.Supply Chain Resilience: Build advanced digital twins that actively learn, leveraging connected asset intelligence to predict mechanical anomalies and streamline live operations.IoT & Operations: Architect contextual, relationship-aware retail recommendation assistants that deep-dive into multi-hop relational history to build AI applications customers can trust.GenAI Commerce: Inject real-time, fraud-enriched detection variables directly into Databricks Genie, allowing organizations to catch deeply nested, multi-tier compliance risks instantly.Financial Crime
Driving Partner Momentum Through Demand Generation
Neo4j partner marketing continues to expand demand generation activity across our strategic cloud and data ecosystem, with programs designed to build awareness, engage new audiences, and create more opportunities for joint customer conversations. Through our Neo4j Connected Intelligence digital event series, we are consistently showing up alongside AWS, Google Cloud, Microsoft, Snowflake, and Databricks to highlight how graph intelligence helps organizations turn connected data into knowledge for analytics, AI, and intelligent applications.
In parallel, our partner hands-on lab workshops with AWS, Google Cloud, Microsoft, and Databricks are giving customers a more practical way to experience Neo4j in action. These sessions are designed to move beyond high-level messaging and help technical audiences see how Neo4j works alongside the platforms they already use. Early programs have shown strong potential for lead generation and opportunity progression, and we are planning additional sessions through the second half of the year and beyond.
Together, these activities are helping raise Neo4j’s profile across the partner ecosystem, amplify our joint presence with strategic cloud and data partners, and give field, SI, and reseller teams more reasons to engage customers around high-priority use cases like AI, fraud detection, customer intelligence, supply chain, and knowledge graphs.
ICYMI:
Deploying Graph Agents in Cortex with Neo4j Graph Analytics for SnowflakeMastering Neo4j Within the Microsoft EcosystemNeo4j AuraDB on Google Cloud: See It in ActionClosing the Context Gap: Agentic AI with Neo4j & Microsoft FoundryGraphRAG in Action: Building Smarter AI Agents with Neo4j and DatabricksNeo4j Agent on Google Cloud MarketplaceZero-Copy Advanced Graphs: Neo4j Data Reasoning Directly in Snowflake
NODES AI Recap: From Data to Knowledge to Action
Our full breakdown from the NODES AI event delivers a masterclass on how graphs transform raw enterprise data into highly accurate, structured knowledge bases for generative AI and complex enterprise workflows. By exploring real-world telemetry, query execution patterns, and vector-graph hybrid models, these sessions demonstrate how to optimize context retrieval, drastically reduce LLM hallucinations, and turn static data architectures into live, action-oriented intelligence engines.
To help technical teams implement these frameworks, we have highlighted key technical sessions from the event:
: Learn how graphs turn massive data from platforms like Snowflake and Databricks into actionable foundations for generative AI, serving as the ultimate translation layer for enterprise data.The Graph Intelligence Platform (Databricks & Snowflake): Discover how to leverage Neo4j Graph Analytics and native Graph Agents directly inside Snowflake to categorize complex data at a multi-terabyte scale without moving the data.Deploying Graph Agents and Native Analytics inside Snowflake Intelligence: Explore how to leverage Neo4j alongside Amazon Bedrock to power explainable, rich multi-hop queries for on-device agentic applications while reducing mobile app complexity.Building Smarter Mobile Apps with On-Device AI Agents and Neo4j GraphRAG: See how to model complex global regulations, cloud assets, and controls as a connected network to enable continuous compliance-as-code and navigate multi-cloud governance rules.Automating Continuous Compliance and Intelligent Regulatory Mapping
Mistral: Building Sovereign AI Stacks for the Global Market
The demand for regional data sovereignty and compliant infrastructure is rapidly scaling, particularly among government, public sector, and strictly regulated enterprise clients. Following our sponsorship of the first Mistral AI Summit in Paris, Neo4j has established a definitive blueprint for building secure, localized “sovereign technology stacks”.
By deploying Neo4j as an independent, robust knowledge layer, organizations can effectively swap, scale, and secure enterprise large language models such as Mistral to meet strict data localization and sovereignty requirements. This framework ensures that corporate IP and sensitive context remain entirely within secure geographic boundaries while still delivering the advanced multi-hop reasoning required for enterprise-grade GraphRAG.
To dive deeper into this joint architectural vision, you can now watch our on-demand session to learn how our engineering teams are bringing secure, sovereign AI deployment to life.
On-Demand Session:Watch the Sovereign AI Stack Webinar** Summit Interview:**Watch the Paris Summit Interview
Heading into H2
As we move into the second half of the year, the mandate for enterprise data strategy is clear: organizations must move past disconnected infrastructure and build unified platforms capable of real-time reasoning. The engineering milestones, native ecosystem integrations, and strategic expansions delivered this quarter lay a powerful foundation for the next wave of enterprise AI adoption.
Looking forward, Neo4j remains fully committed to working alongside our global partner network to help organizations bridge the gap between complex cloud data structures and action-oriented intelligence.