Pierre Halftermeyer retweeted
#Prévention 🌳🔥 | La lutte contre les feux de forêt se prépare dès maintenant.
ℹ️ Le débroussaillement : une obligation légale indispensable pour diminuer l’intensité des feux, limiter leur propagation et protéger les habitations.
En savoir plus sur gironde.gouv.fr/Actions-de-l…
Pierre Halftermeyer retweeted
Folks, today we're announcing the acquisition of @graph_aware, moving up to the stack, and taking on black-box intelligence tools like Palantir Gotham. Let's go! 🚀
For 10+ years GraphAware has been our partner. They're a leader in mission-critical intelligence analysis. Together, as one organization, we're building an open-standards alternative to black-box intelligence tools.
1️⃣ The database belongs to them
2️⃣ The data belongs to them
3️⃣ Every choice they make with it belongs to them
Nothing important is hidden behind proprietary logic. They can see how data moves, understand how results are produced, and trust the system they rely on. This is not theory, this is not a vision, this is not hypothetical. What's so wonderful about GraphAware Hume is that it's already deployed by the Western Australia Police, the European Commission and government agencies across the US, UK, EU, and Australia.
Looking ahead, we're building the best possible open, AI-powered intelligence platform that agencies can deploy on their own terms, own their data on, and build on for years to come. One open-standards stack. Deeper querying. Better performance at scale. A tighter integration between analysis and data.
Welcome, GraphAware!
Neo4j has always used synchronous I/O. Every read or write block the thread until the OS finishes. Simple, predictable, and a real bottleneck in cloud environments where storage latency is higher.
2026.04 changes that. Async I/O via io_uring is now available for the background page evictor and checkpointer on Linux, meaning Neo4j can issue requests without waiting and keep working while storage catches up.
It's Enterprise only, requires liburing and JDK 25+, and is off by default. One config line to turn it on: server.memory.pagecache.async=true
more:
bit.ly/3QNHo9C
Going to #GoogleCloudNext?
1️⃣ Visit our booth #2717 to see cool demos and talk to our experts about how graphs can ground your #LLMs with accurate context
2️⃣ Request a meeting with our specialist to get your data AI-ready
3️⃣ On April 23, we are hosting an executive dinner together with @Databricks - make sure you request to attend!
@googlecloud @gcloudpartners
bit.ly/4doecPJ
Neo4j Live: Neo4j - The Definitive Guide xitter.gq/i/broadcasts/1oKMvRdBy…
Announcement 🎊 GRAPH TYPE is now in Public Preview in #Neo4j 2026.02!
What this means:
✓ Strict data quality - Enforce property types, mandatory fields, label implications, and relationship endpoints in one place
✓ Flexible governance - Validate your core model without sacrificing Neo4j's schema-flexibility for ad-hoc evolution
✓ Simple lifecycle management - Four commands handle everything: SET, ADD, ALTER, and DROP
Read more: bit.ly/4sxoeCl
Building AI-ready systems starts with the right data foundation.
Join Jesús Barrasa for a webinar and learn how domain modeling and ontology integration strengthen graph schema design by embedding meaning, context, and shared understanding directly into your knowledge graph.
You’ll discover:
• How domain modeling and ontologies create scalable, semantically rich data foundations
• Why a formal semantic layer improves consistency, clarity, and interoperability
• How #AI can accelerate modeling, enhance semantic alignment, and support schema evolution
Whether you’re designing a new graph or evolving an existing one, this webinar will help you build connected data models that are truly AI-ready.
Asia Pacific: bit.ly/3MUGOFj
Europe: bit.ly/4sfMxoh
Americas: bit.ly/40C956I
🔍 Neo4j v2026.01 introduces #Vector search with filters as a preview feature: you can apply predicates inside the vector index at query time so the index behaves like it only contains vectors that match your criteria.
Helping with:
-latency low
-relevant results
bit.ly/4qZW5Dy
Insights at scale with Infinigraph 🚀
We are thrilled to announce that #Infinigraph is now generally available and helping companies with billions of data points, allowing:
👉 Building Massive Knowledge Graphs.
👉 Supporting Agentic AI: Provide AI agents with "long-term memory" that grows with your business.
👉 Giving real-time context: Deliver structured context to #LLMs in milliseconds.
Need to scale? Read how Infinigraph can help.
bit.ly/3NQLkVr
#Neo4j
Pierre Halftermeyer retweeted
Building knowledge graphs for #GenAI? Start with this webinar.
Discover how to use ontologies and LLMs to create scalable graphs for #GraphRAG, intelligent search, and analytics.
Jesús Barrasa will also do a live demo!
Asia Pacific: bit.ly/4oF6B1b
Europe: bit.ly/3K9zpAU
Americas: bit.ly/4pkfkHj
#Neo4j
Pierre Halftermeyer retweeted
Optimize your WCC algorithm projections!
👌 Best practices
👌 Tips
👌 Real-world impact
Make your graph analytics faster and more efficient by Maxime Guery, PhD & Consultant Engineer, Neo4j
bit.ly/48kSRnF
Pierre Halftermeyer retweeted
Connected Component Forest for fraud detection: events are linked chronologically to preserve real-time causality and avoid information leakage in ML pipelines.
The result? Time-aware graphs track connections as they form.
Master fraud detection with Pierre Halftermeyer's Temporal Graph Modeling:
bit.ly/45ecnPz
#FrauddDetection #GraphDatabase #Neo4j
Pierre Halftermeyer retweeted
Simpler queries, faster performance, and the ability to tackle difficult graph problems with ease; let's see #Cypher 25 can do it!
What does it offer?
⌨️ REPEATABLE ELEMENTS match mode and allReduce function
Here's a hint of what you'll find in this blog: Cypher 25 helps you tackle complex graph problems.😉
Review how with Pierre Halftermeyer bit.ly/46XVFX1
#Neo4j
Pierre Halftermeyer retweeted
🤖 @emileifrem, co-founder and CEO @neo4j: Architecting Agent Memory with Connected Data
Pierre Halftermeyer retweeted
Mastering Fraud Detection With Temporal Graph Modeling
Did you know that consumer credit fraud costs the financial industry more than $10 billion annually?
Traditional methods struggle to keep pace with evolving fraud networks, prompting us to explore a dynamic and scalable solution: temporal graph modeling.
This post dives into the problem and proposes an innovative data model.
It builds a scalable temporal graph model to compute point-in-time connected components in a User-Event-Thing graph in real time.
By @halftep @neo4j
medium.com/neo4j/mastering-f…
#Analytics #FraudDetection #Finance #GraphDB #DataScience #DataModeling #Temporal #RealtimeData
--
The Year of the Graph's next newsletter on all things Knowledge Graph, Graph Analytics / Data Science / AI and Semantic Tech is due in Autumn 2025.
Subscribe and follow to be in the know. Reach out if you'd like to be featured 👇
yearofthegraph.xyz/newslette…
Pierre Halftermeyer retweeted
Pattern matching: the most straightforward and effective technique for solving real-world data problems.😮
Some use cases where you can apply pattern matching to your data:
👉Personalize Product Recommendations
👉Optimize Supply Chains
👉Detect Fraud (take a look at the fraud ring detection below)
Data alone won’t solve your business problems—it’s how the data is connected that reveals value. Learn more: bit.ly/4lQRam8
#Neo4j #Graphdatabase
Pierre Halftermeyer retweeted
Working with legal contracts has never been easier: implement an end-to-end solution using Agentic GraphRAG.
1️⃣ Structure legal contracts into a knowledge graph in Neo4j
2️⃣ Create a powerful repository of information that’s easy to query and analyze.
3️⃣ Build a LangGraph agent that allows users to ask specific questions about the contracts, making it possible to rapidly uncover new insights.
How? 👇
bit.ly/4jlki39
#GraphRAG #neo4j #knowledgeGraphs
Pierre Halftermeyer retweeted
Interesting post on Klarna's approach to AI: eliminating SaaS and dumping everything into @neo4j, then layering LLMs on top.
Yes, we did shut down Salesforce a year ago, as we have many SaaS providers—an internal estimate is about 1,200 SaaS shut down.
No, I don't think it is the end of Salesforce; might be the opposite.
Here is what actually happened and how/why we originally intended to NOT share it publicly:
At Klarna, we decided early to explore the potential of AI and LLMs—mostly ChatGPT—while being open to testing all things that seemed to be trending.
We encouraged all employees to do so and allowed them to pursue ideas organically rather than following "management direction" on exactly what they should be building.
In the early days of ChatGPT, we heard a lot:
"this tool allows you to feed all your PDFs, all your data sources to a LLM!"
However, the old universal truth of data scientists still holds true, even in AI: "shit in, shit out."
Feeding an LLM the fractioned, fragmented, and dispersed world of corporate data will result in a very confused LLM.
We started instead exploring a few key concepts: What of our data was actually valuable? What data was duplicative, incorrect, or contradicting? Why was it like that?
While people nowadays can criticize things like Wikipedia, we also reflected on the fact that it is a remarkable achievement—having over 20,000 people collaborate on the largest graph of knowledge that is still fundamentally of high quality, accessibility, and accuracy. What could we learn from this?
A Swedish company, @neo4j, and @emileifrem introduced us to the beautiful world of graphs.
We further explored data modeling, ontology, and, of course, vectors, RAGs, and many things.
Key to our explorations became the conclusion that the utilization of SaaS to store all forms of knowledge of what Klarna is, why it exists (docs), what it tries to accomplish (slides, tickets, kanban boards), how it is doing (sheets, analytics), who is it dealing with (CRM, supplier management), who works here (ERP, HR) and what it has learnt was fragmented over these SaaS—most of them having their own ideas and concepts and creating an unnavigable web of knowledge that required a tremendous amount of Klarna specific expertise to operate and utilize.
We also recognized that enterprise software has a standard set of features that are vital for it to operate—features such as audit, versioning, access and edit management, and similar universal needs. We need them as well, but that fragmentation again adds friction, admin overhead, and more.
So, we decided to start consolidating; to put things together, connect our knowledge, and remove the silos. The side consequence of this was the liquidation of SaaS—not all of them, but a lot of them. And not for the license fees, even though those savings have been nice, but for the unification and standardisation of our knowledge and data.
So no, we did not replace SaaS with an LLM, and storing CRM data in an LLM would have its limitations. But we developed an internal tech stack, using Neo4j and other things, to start bringing data=knowledge together.
Ultimately, we found this very interesting, but more importantly starting seeing serious productivity gains. We allowed our internal AI to use this knowledge, and we realised with the help of @cursor_ai we could quickly deploy new interfaces and interactions with it.
So, I discussed with one of my board members: should we share this publicly?
We decided not to. We hold no grudge against SaaS (not true—I hate some of it, but won't tell you which one). But we are a payments company and a neo bank, there is limited value for us to share this externally.
However, Klarna, being a bank, holds quarterly calls with its investors, and in passing on of these calls, I mentioned that we had removed some SaaS software including Salesforce. It turns out that the recording was leaked to @SeekingAlpha, and they put out a news post about it. And from there, it went crazy.
Suddenly, @Benioff was asked on stage why Klarna was leaving Salesforce. I was tremendously embarrassed.
So, to summarise, what does this mean? Will all companies do what Klarna does? I doubt it. On the contrary, much more likely is that we will see fewer SaaS consolidate the market, and they will do what we do and offer it to others. Those are likely to be your next SaaS.
And it is very likely that Salesforce will be one of those companies. As highlighted many times, they do so much more than CRM today and hence have the opportunity to become that hub of knowledge that modern companies will seek.
But there are also risks for them and others; a lot of our large enterprise SaaS providers suffers from a fallacy. They started as companies with a clear opinion of how to do things, but over time, as they try to satisfy every whim of any random person working at any large enterprise, they become somewhat of a glorified database and lose their opinion. Opinionated software is worth something, as opinions represent an experience of what works, what produces results. And this is the ultimate value.
So I hope with sharing this we can clarify a lot of speculation and misunderstandings and in the end same thing as is always true, just like when mobile came along, we talked about mobile first, now you need to be AI first. Of course all SaaS companies will need to learn adopt and evolve. But if they do there is tremendous opportunity ahead.
Pierre Halftermeyer retweeted
Yes, we did shut down Salesforce a year ago, as we have many SaaS providers—an internal estimate is about 1,200 SaaS shut down.
No, I don't think it is the end of Salesforce; might be the opposite.
Here is what actually happened and how/why we originally intended to NOT share it publicly:
At Klarna, we decided early to explore the potential of AI and LLMs—mostly ChatGPT—while being open to testing all things that seemed to be trending.
We encouraged all employees to do so and allowed them to pursue ideas organically rather than following "management direction" on exactly what they should be building.
In the early days of ChatGPT, we heard a lot:
"this tool allows you to feed all your PDFs, all your data sources to a LLM!"
However, the old universal truth of data scientists still holds true, even in AI: "shit in, shit out."
Feeding an LLM the fractioned, fragmented, and dispersed world of corporate data will result in a very confused LLM.
We started instead exploring a few key concepts: What of our data was actually valuable? What data was duplicative, incorrect, or contradicting? Why was it like that?
While people nowadays can criticize things like Wikipedia, we also reflected on the fact that it is a remarkable achievement—having over 20,000 people collaborate on the largest graph of knowledge that is still fundamentally of high quality, accessibility, and accuracy. What could we learn from this?
A Swedish company, @neo4j, and @emileifrem introduced us to the beautiful world of graphs.
We further explored data modeling, ontology, and, of course, vectors, RAGs, and many things.
Key to our explorations became the conclusion that the utilization of SaaS to store all forms of knowledge of what Klarna is, why it exists (docs), what it tries to accomplish (slides, tickets, kanban boards), how it is doing (sheets, analytics), who is it dealing with (CRM, supplier management), who works here (ERP, HR) and what it has learnt was fragmented over these SaaS—most of them having their own ideas and concepts and creating an unnavigable web of knowledge that required a tremendous amount of Klarna specific expertise to operate and utilize.
We also recognized that enterprise software has a standard set of features that are vital for it to operate—features such as audit, versioning, access and edit management, and similar universal needs. We need them as well, but that fragmentation again adds friction, admin overhead, and more.
So, we decided to start consolidating; to put things together, connect our knowledge, and remove the silos. The side consequence of this was the liquidation of SaaS—not all of them, but a lot of them. And not for the license fees, even though those savings have been nice, but for the unification and standardisation of our knowledge and data.
So no, we did not replace SaaS with an LLM, and storing CRM data in an LLM would have its limitations. But we developed an internal tech stack, using Neo4j and other things, to start bringing data=knowledge together.
Ultimately, we found this very interesting, but more importantly starting seeing serious productivity gains. We allowed our internal AI to use this knowledge, and we realised with the help of @cursor_ai we could quickly deploy new interfaces and interactions with it.
So, I discussed with one of my board members: should we share this publicly?
We decided not to. We hold no grudge against SaaS (not true—I hate some of it, but won't tell you which one). But we are a payments company and a neo bank, there is limited value for us to share this externally.
However, Klarna, being a bank, holds quarterly calls with its investors, and in passing on of these calls, I mentioned that we had removed some SaaS software including Salesforce. It turns out that the recording was leaked to @SeekingAlpha, and they put out a news post about it. And from there, it went crazy.
Suddenly, @Benioff was asked on stage why Klarna was leaving Salesforce. I was tremendously embarrassed.
So, to summarise, what does this mean? Will all companies do what Klarna does? I doubt it. On the contrary, much more likely is that we will see fewer SaaS consolidate the market, and they will do what we do and offer it to others. Those are likely to be your next SaaS.
And it is very likely that Salesforce will be one of those companies. As highlighted many times, they do so much more than CRM today and hence have the opportunity to become that hub of knowledge that modern companies will seek.
But there are also risks for them and others; a lot of our large enterprise SaaS providers suffers from a fallacy. They started as companies with a clear opinion of how to do things, but over time, as they try to satisfy every whim of any random person working at any large enterprise, they become somewhat of a glorified database and lose their opinion. Opinionated software is worth something, as opinions represent an experience of what works, what produces results. And this is the ultimate value.
So I hope with sharing this we can clarify a lot of speculation and misunderstandings and in the end same thing as is always true, just like when mobile came along, we talked about mobile first, now you need to be AI first. Of course all SaaS companies will need to learn adopt and evolve. But if they do there is tremendous opportunity ahead.
Pierre Halftermeyer retweeted
Effortless RAG With Text2CypherRetriever
Excited to introduce the Text2CypherRetriever!
Are you an experienced #Neo4j user or just getting started? Don't worry: the Text2CypherRetriever can significantly streamline your development process.
Take a look: bit.ly/3AQEVn3
#Text2Cypher























