Appreciate the thoughtful questions.

1. I'll address this in two parts.

(a) Memory vs. Enterprise Search. I consider search to address targeted, stateless retrieval whereas memory solves temporal, tacit, and derived problems. Glean can tell you why a ticket was filed or answer a specific question regarding a customer call. But in many companies, important questions are broader: "What went wrong the first time we went with this vendor?" "How has our brand shifted in tone over time?". These cannot be answered by a few documents, and it's not obvious whether this information would be in Slack or Notion or Drive. It requires an active, entropy-fighting system that is going to extract information and keep track of how it evolves over time.

(b) Benchmarks: absolutely. Don't want to claim anything before we've published results, but Hyper scores very well on LoCoMo and LongMemEval, and we are constantly trying to bolster our set of evals. We will publish results more openly in the coming weeks. I will caveat though: many SOTA memory providers are converging on the top end of these benchmarks, and yet we don't see mass adoption. We believe that UX affordances are underrated and critical to get "company brains" working in real, messy businesses. Many of our users have come to us from other providers purely because the competition was too difficult to use and maintain across the org.

2. Hyper maintains a graph of information where each node is an extracted "fact." This happens continuously, in the background, live from every connector or connected agent. At insertion-time, new information is compared against relevant information. Our system (a DAG of agentic nodes) determines the relationships between these facts and makes appropriate updates: X derives Y, A updates B. For now, we rely on recency as the primary indicator of conflict (i.e. we assume more recent information is generally more true than old information). We realize that this will need to become more sophisticated, and are iterating.

3. Knowledge extraction is real-time and asynchronous, and should add next to zero latency to any existing system. We continually update the graph in our backend, without relying on a nightly compaction/dreams cycle, so information from the world should be reflected in Hyper's responses in close to real time. Retrieval can be slightly more expensive, but the latency is negligible compared to the overhead of the calling agent. We recognize the importance of performance (we both worked on on-device robotics!) and are happy to publish numbers as we measure them :)

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esafak 9 days ago | parent [-]

I made a mistake; apparently Glean uses knowledge graphs too: https://www.glean.com/resources/guides/glean-knowledge-graph

This raises a follow-up question: what is your differentiation?

	▲	kanyesrthaker 9 days ago \| parent [-]
		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.