New Ways to Scale and Apply Enterprise AI with Neo4j
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… Read more →
Read the full story on Neo4j BlogHave you worked with this?
The story is what was announced. Nobody has discussed it yet, so if it touches your team, a short post about what you’ve seen helps the next reader.
More from Neo4j
Recent updates from Neo4j, so you can tell whether this is a one-off or part of a pattern.
Who controls your intelligence analysis platform?
Over the years, I’ve seen two broad philosophies emerge for building an intelligence analysis platform. One concentrates knowledge and capability inside the vendor. The other builds them inside the customer’s own team. The distinction matters more as the platform grows.… Read more →
Beyond the Banana: Driving real-time recommendations with graph-grounded Copilots in Microsoft Fabric
Retailers rarely lack data. Between customer profiles, point-of-sale transactions, inventory levels, and promo schedules, modern enterprises generate terabytes of signals daily. The issue is that legacy relational architectures keep this information locked in siloed systems across POS platforms, inventory systems,… Read more →
Massive Parallel Imports in Neo4j Without Deadlock and Lock Contention
Optimizing graph import partitioning based on workers and k-1 coloring algorithm.This article is a generalization of the approach designed by Eric MONK in his article “Mix and batch: a technique for fast, parallel relationship loading in Neo4j”. It explains the… Read more →
More in Databases
What other companies in Databases are doing. The category page shows who’s active, side by side.
Can your Postgres survive a bad query?
How do Postgres providers handle a query that exhausts memory? A recursive query benchmark compares query failures and cluster survival across ClickHouse Managed Postgres, Cloud SQL, PlanetScale, and Amazon RDS.
chDB Durable Layer for agent memory
The chDB Durable Layer keeps agent memory fast to query locally while making its analytical state recoverable across laptops, CI jobs, and short-lived sandboxes.
Handling hot shards
What makes a good sharding strategy today might not work so well tomorrow. What happens when you have one tenant hitting your database orders of magnitude more than any other? Or product managers knocking down your door with new requirements? If you're already on Neki, fortunately these are issues we can solve.
How to add hybrid search to a Postgres app with Pinecone in 2026
Add hybrid search to a Postgres app with Pinecone: full-text and vector search in one index, Postgres as the source of truth. Built around a snack shop example.