4 min read
As AI initiatives grow beyond a first use case, the conversation starts to change. It’s no longer just about getting a model working. Questions about running AI at scale and applying it to complex problems like financial crime become just as important.
That’s the direction we’re headed with these updates. They focus on what it takes to support AI applications as they grow in production, while expanding the kinds of problems organizations can solve with Neo4j.
Solving industry problems with graph intelligence
Some of the hardest problems organizations face are deeply specific to their industry. Solving them takes more than the underlying technology. It takes an understanding of the domain, data, patterns, and workflows that shape the problem itself.
Financial crime is a good example. Fraudsters operate as connected networks, moving across accounts, identities, devices, and organizations to hide their activity and avoid detection. Yet while financial criminals exploit the gaps between silos, financial crime teams still work within them, with data, tools, and investigations fragmented across the organization.
Neo4j GraphAware Financial Crime Intelligence (FCI) is built to close that gap. Predefined, graph-powered detection rules surface patterns that conventional approaches miss, and every alert is enriched with the people, entities, and relationships behind it, all connected in a single entity-resolved knowledge layer.
Investigators can tell real risk from noise faster, spending less time on false positives and more judgment on the cases that need it. Evidence, context, and provenance are preserved in the graph, so decisions can be explained and reviewed later. It’s designed to build on what already works, without requiring a wholesale replacement of the existing stack, giving teams a pragmatic path to more connected financial crime detection and investigation.
Scale AI into Production
Getting an AI application into production is only part of the journey. As organizations move beyond their first applications, the challenges start to shift. More workloads, more environments, and more data all increase the complexity of running AI at scale.
One part of that challenge is working with data spread across different systems without having to move it first. Neo4j Virtual Graph, currently available in Preview, moving to General Availability in the next couple of months, brings zero-copy graph capabilities to data where it already lives, letting organizations build and work with knowledge graphs without first moving that data into Neo4j.
We’re bringing together several capabilities to help organizations manage that complexity. For customers running many applications, tenants, or environments, multiple databases support lets them consolidate workloads as individual databases within a single Neo4j AuraDB instance while keeping each one separate. That can reduce both cost and administrative overhead as the number of databases grows**.** If you’re managing data for multiple end customers, each can have their own database within the same AuraDB instance. With full support in the Aura API, provisioning can also be fully automated end-to-end. Larger AuraDB configurations further extend that consolidation, now supporting up to 2TB of RAM and 5TB of storage for growing workloads. That headroom can serve a single large database or many smaller databases on the same instance
Multiple databases in AuraDB Business Critical
As deployments grow, managing more workloads is only one part of the challenge. Keeping critical data available when a cluster or region goes down matters too. Now generally available in Neo4j Graph Database Enterprise Edition, with AuraDB support also planned, cross-cluster database replication lets organizations keep a continuously updated copy of a database in a separate cluster, ready to take over when needed.
Scale also changes what organizations need from the deployments themselves. Releasing in Preview with an Early Access Program in September, Neo4j Graph Analytics lets self-managed customers scale graph analytics independently of the operational database, so larger analytics workloads don’t have to compete for the same resources.
Together, these capabilities address different dimensions of scale as organizations move AI applications into larger production environments.
Bringing It Together
Taken together, these updates reflect where Neo4j is continuing to invest. Solutions like GraphAware FCI bring graph technology and domain knowledge together around specific industry problems, while new scaling capabilities help support those applications as they move into production, across larger workloads and more complex environments.
Each update stands on its own, but they share a broader focus on helping organizations apply and scale graph technology to the problems that matter most.
Learn More
- Neo4j GraphAware Financial Crime Intelligence blog
- Join us for a LinkedIn Live session covering Neo4j GraphAware Financial Crime Intelligence on October 13th at 8 a.m. PT, noon ET, 6 p.m. CET
- Scale AI into Production [blog](https://neo4j.com/blog/auradb/scale-ai-into-production/)
- AuraDB [documentation](https://neo4j.com/docs/aura/)
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