We've been working on gov/enterprise/etc projects here as part of louie.ai that are coming to a head -- I'd expect not far from what XAI, Perplexity, and others are also doing. However, while those must focus on staying cheap at consumer scale, we focus on enterprise scale & ROI, so get to make different trade-offs, and I'm guessing closer to how Google and other more mature teams do KG. We're not doing traditional KG, however -- it's a needlessly/harmfully lossy discretization -- but are coming from lessons in that world, esp in the large-scale intel/OSINT side and graph neural net community.

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!