Fair enough, i was drawing the comparison between traditional enterprise search and what we do. There are several companies that borrow the graph-based data structure; this part is not so unique. They do have different methods for how that information is orchestrated, but I think I would reframe a bit: the end user problem does not start and stop with the memory algorithm and technical layer.

The main thing we see in the world is that (a) teams already struggle to coordinate information over many different personalities and data sources. This was a more dull problem before when the actual IC/execution overhead was so large. But now with AI the execution overhead is way smaller, and "being on the same page" is a much bigger problem. (b) As agents do more and more of the mechanical work in the company, it's vital that they have a consistent big picture-view to perform tasks efficiently without errors.

Hyper aims to solve this problem end-to-end; the memory system is a vital part of this, but Hyper does more. We already support native agentic email-writing and LinkedIn-drafting automations, and will be expanding on that front. Today it's a "brain that knows everything," but so much of the value is in using that brain to perform work in a self-improving way. And on the other side, we need to make sure that getting information into the system is as frictionless as possible. We care a ton about UX -- one-click integrations, using hooks to get context in and out invisibly and reliably.