I've always thought that knowledge graphs/expert systems, and even the broader concept of entity-attribute-value storage, got an unfairly bad reputation because of the 1970s/1980s "AI Winter."

And I think that perhaps this reputation is why so much of the oxygen in the RAG space has been consumed by the notion that "RAG = retrieval of fragments by vector similarity."

The difference now from decades ago, of course, is that now LLMs can do both the job of maintaining that graph at scale, and being able to agentically run successive queries to explore for best practices in any situation! And these have reached the scalability where any small business can build and use their own expert system.

I really want to see this approach win, because I think there's such an opportunity to explore even more data structures and approaches from the past and how their impact can be reimagined. If LLMs do indeed approach AGI, it will be in large part due to the ability to use tools (there's some evolutionary irony there, too) - and we should be trying every kind of underlying storage for those tools that we can, standing on the shoulders of giants.

(And curious what database you use for the knowledge graph - those are also a place where we stand on the shoulders of giants!)

That said, this is the ultimate moat. Once everything about how to operate a business lives in your product, the business must rely heavily on it. I personally would only use something like this if I knew it was open source and that data could live on my own servers. If agents and my own team are consulting Hyper for things and you go out of business or move upmarket or something, it's pretty much back to the stone age for us.

Very useful idea though with a lot of potential, especially for companies like OpenAI and Anthropic looking for a moat!

You loose sooooooo much meaningful context and information when you transform something into a knowledge graph. Simple cases like "Gabe is CEO of Valve" map nicely to a graph, but things like "Matt Garman is CEO of AWS" don't represent that AWS is a sub-company of Amazon (with it's own CEO).

Additionally, one of my biggest gripes of Claude's memories and every memory system I've worked with is they completely fail to capture intent. The architecture notes I documented while doing a wild spike on a critical infrastructure component absolutely should not be referenced in every day work. Yet, somehow, that type of memory always works it's way into unrelated sessions.

I live in Japan, and here, the National Diet Library and the Statistics Bureau of the Ministry of Internal Affairs and Communications are working on RDF. Are other countries working on it too?

How well would it work for a company that has the kinds of artifacts that you mentioned, but that lacks the quality/precision/accuracy that Hyper might need?

I'm especially thinking about mature companies that don't sell software and that have made a lot of mistakes and memorialized inconsistencies.

Is Hyper just a bad fit for them? Or could there be a path toward refinement and correction that eliminates ambiguities, etc.? For companies like this, Hyper could provide the clarity that such a company lacks day-to-day.

How are you handling cases where multiple sources of truth contradict each other?

Does Hyper assume best guess or is there any human in the loop verification?

This looks great and congratulations on the launch.

I am also building in this space and wanted to get your views on a few things.

1. Are you building your own connectors to 3p systems? 2. How are you finding the sales motion? I found people to get the problem fast, but actually converting them seems rather slow.

Good luck!

2. How do you deal with conflicting facts? In tech, the new is constantly replacing the old.

3. Is knowledge extraction real time? How fast is it in general?

Made me think this was for companies working on self-driving.