A bit more concretely, for the LLM era, we're especially oriented around the move from vanilla RAG chunking to graph RAG, hierarchical RAG, "auto-wiki" style projects, and continuous learning LLMs. Separately, we've been working on neurosymbolic query synthesis for accessing this and mixing in (privacy-aware) continuous learning from teams using it. I think the first public details on this were in my keynote at the 2024 Infosec Jupyterthon, and we'll be sharing more at graphtheplanet.com next week as well. We haven't said as much, but we're also looking at the problem that the data itself isn't to be trusted, e.g., blind men and the elephant reporting different things over time on the news/social media/IT incident tickets.
Right now we're just focusing on building and delivering great tech, customer problem by customer problem. There's a lot to do for a truly good end-to-end analyst/analytics experience!
- The effort needed for KGs has higher general potential value because RAG is useful
- The KG ontology quality-at-scale problem is now solvable by LLMs automating index-time data extraction, ontology design, & integration
This is an area we're actively looking at: Self-determining ontologies for optimizing RAG, such as during evolving events, news, logs, emails, customer conversations. We have active projects across areas here already like for emergency response, cyber security, news mining, etc. If folks are interested here, we're definitely looking for design partners with challenging problems on it. (And, looking to hire a principal cybersecurity researcher/engineer on it, and later in our other areas too!)