Cascadia Global Ventures

In the race to uncover insights from complex data, enterprises are turning to graph analytics. Graphs offer a powerful way to map relationships, detect patterns, and act on connections that other models miss. But one uncomfortable truth lurks behind the rise of mainstream graph databases: most of them are giving you yesterday’s news.

What do we mean by this?  There is a problem with persisted graphs. Legacy graph databases treat the graph as a data store. You load the graph from source data, shape it into a schema, persist it — and then query it later.

But here’s the catch: data always changes. Relationships evolve. Nodes disappear. New edges emerge. By the time you run your query, your graph is already out of date.

This isn’t just a performance problem. It’s a correctness problem. Even a single missing relationship in a highly connected graph can invalidate an entire query. In domains like cybersecurity, supply chain, and fraud detection, those misfires are not academic they carry the risk of being catastrophic.

A graph is a view, not a store. Which means a different approach is needed. This is where we, at Rocketgraph, come in. Our philosophy is that graphs should be living, on-demand reflection of reality. That means:

You don’t persist it
You don’t reload it manually
You don’t write complex ETL to prepare it

Instead, you generate it every time you need it, from your source(s) of record.

Our Mission Control front end with LLM-driven, no-code interface, and agentic AI connects to your data source, infers a schema, and builds the graph automatically. The result is always complete, always fresh, and always correct. No code. No waiting.

The Rocketgraph Performance Lab benchmarks yielded results demonstrating that Rocketgraph outperforms graph databases like Neo4j by up to 500x on real-world cybersecurity queries. With the Rocketgraph approach graphs are generated when needed and as a result:

There’s no missing recent data.
There’s no schema drift.
There’s no chance your analytics are stale.

Rocketgraph’s  entire system is built on a high-performance, parallelized in-memory engine — inspired by our DoD supercomputing roots — that is why we can operate at planetary scale – literally processing hundreds of billions of edges.

If you’re using a persisted graph database, ask yourself: When was the last time you reloaded your graph? If you’re not sure, you’re probably not working from the truth. So, make sure your graphs are a living, on-demand reflection of reality so you are always right.