Sponsored Content material by Rocketgraph
Rocketgraph, born out of a high-performance computing mission on the U.S. Division of Protection, is rewriting the principles of graph analytics. By merging generative AI (GenAI) with supercomputing-grade efficiency, Rocketgraph allows analysts to ask subtle questions in plain English—and obtain solutions from probably the most advanced, high-volume datasets in seconds.
Organizations throughout authorities and trade face a brand new class of perception discovery issues. Whether or not it’s tracing the origins of economic fraud, navigating multi-year cyberattacks, or synthesizing various information for precision medication, the widespread problem is that this: How do you make sense of all of it, quick?
Graphs are uniquely suited to this drawback. Not like conventional databases that retailer information in tables, graph analytics makes use of nodes and edges to signify relationships—perfect for advanced techniques like communications networks, provide chains, or legal networks. However traditionally, graph analytics has been the province of specialists. Operating a question typically meant writing customized code in languages like Cypher or Gremlin, constructing a schema from scratch, and ready days (or weeks) for outcomes.
Two issues have modified that. First, GenAI can now generate graph queries and construct schemas routinely based mostly on consumer prompts. Second, platforms like Rocketgraph use HPC ideas—similar to scale-up structure and in-memory processing—to get rid of the efficiency bottlenecks that plagued legacy graph databases.
Legacy graph instruments scale by distributing information throughout nodes in a community. Whereas positive for easy fraud checks, this “scale-out” structure hits a wall when performing multi-hop queries throughout massive datasets—like monitoring a lateral motion assault that unfolds over months. Rocketgraph flips this mannequin on its head.
As a substitute of scaling out, Rocketgraph scales up—preserving large graphs in reminiscence on a single node and utilizing multithreaded parallelism to ship leads to actual time. Which means a safety analyst in search of a T1550.002 sample (a recognized technique of credential misuse within the MITRE ATT&CK framework) can merely ask a natural-language query, and the system will return a full sample match—together with all nodes and edges—in seconds.
This isn’t theoretical. Rocketgraph is already powering use instances like:
- Detecting fraud throughout billions of economic transactions
- Monitoring adversarial actors on the darkish net
- Forecasting provide chain disruptions earlier than they occur
- Monitoring genetic interactions in drug discovery pipelines
And since Rocketgraph helps pluggable GenAI, organizations can use their very own LLMs to guard proprietary data whereas enhancing evaluation. The outcome: extra correct insights with better operational safety.
Rocketgraph isn’t only for PhDs in information science. Mission Management, the corporate’s pure language interface, lets any analyst have interaction with the graph with out writing a line of code. Ask a query. Refine it. Visualize the outcome. Iterate. Analysts don’t want a “question man” or a devoted graph modeling crew—they will work together with information instantly.
This shift—from area of interest tooling to accessible intelligence—marks a turning level. As data graphs change into foundational to agentic AI workflows, organizations will want platforms that mix pace, scale, and ease.
Graph analytics not requires a rocket science finances—or a rocket scientist to run it. With Rocketgraph, organizations can lastly analyze probably the most sophisticated datasets on earth with the benefit of pure language and the pace of supercomputing. It’s not only a new option to do graph analytics—it’s a brand new option to assume.
Go to rocketgraph.com to schedule a demo, or obtain our paper right here.
