alt Hacker NewsLaunch HN: Hyper (YC P26) – Company brain to power agentic development
Hey HN, we’re Shalin & Kanyes, best friends who've been hacking together for 10+yrs, and now founders of Hyper (https://heyhyper.ai/). Hyper is a shared “company brain” that plugs into information flowing inside a company to make AI agents and automations better and ultimately save people time.
Models have gotten good enough that they can (mostly) take on long-horizon, complex tasks. We believe the bottleneck now is that these smart-enough models often lack information about your company, which is scattered in people's heads, Slack threads, stale docs, and in back-and-forth convos with AI.
MCP is useful for getting some info in front of an agent, but there are problems: (1) Once the session dies, so does the insight, so instead of copy-pasting a whole doc each time you're telling the agent to dig through Drive each time - not much of a win; (2) Even when MCP works, what it gathers isn't comprehensive, because people decide things on a whiteboard, brainstorm out loud, post a little in Slack, and scribble the rest in a doc, which leaves the agent working from partial information; (3) And even if it had everything, it doesn't do the meta-reasoning required to do a great job. If you paste in a Notion doc and it won't learn your design taste or your writing style unless you tell it to, and it won't know why a decision was made or when.
As undergrads 5 years ago, we were into the tools-for-thought wave and became power users of Notion, Obsidian, Roam, Anki, real believers in building a second brain. After GPT-3.5 came out we started to realize how much more powerful that second brain could be if an AI could actually read it, because suddenly it would know our backstory, our taste, our preferences, and unlock genuinely new capabilities. That’s why we’re building Hyper.
We know it’s not for everybody! But for people who do want to be on the cutting edge, this is a force multiplier that makes agents faster and better. It increases the number of tasks they can do, and how effectively they do them.
Hyper works by ingesting everything you give it access to, Docs, Slack, Email, Calendar, Granola, and synthesizes it into a knowledge graph of facts and their relationships with embeddings for semantic search. The memory system we’ve built is hybrid, with two modalities. Episodes are the raw source items kept as the source of truth. Facts are the meaning pulled out of each episode, stored as subject-predicate-object records with a plain summary and timestamps for when the fact was introduced and when it was invalidated (subject=person, predicate=works_at, object=company). Facts form a graph with typed edges between them: X is in tension with Y, A is derived from B, J supersedes K. Every time a new fact comes in we update the facts in its neighborhood, so the graph stays current, and that's how we handle stale information. When "we'll ship Friday" is later contradicted by "we're shipping Monday," the new fact supersedes the old one instead of both looking equally true, and we never auto-discard the superseded version, so you can still ask how you landed on Monday.
Every fact carries provenance back to its source and access-control tags for who is allowed to see it. At retrieval we query-expand, then fuse semantic search over embeddings with Postgres full-text search using reciprocal rank fusion, and we only ever evaluate a query against the facts and episodes that person has access to, which means two people on the same team can ask the same question and get different answers. We keep information fresh with webhooks where they exist and polling where they don't, hashing contents to catch changes for sources that don’t handle native dedupe. Agents read and write through two paths: lifecycle hooks in tools like Claude Code, Cowork, Codex, and Cursor, where we inject relevant context on every prompt and pull interesting facts out of every response, and plain MCP tool calls for everything that doesn't expose hooks.
We love it! and so do our early users: one CEO uses Hyper to draft emails in his voice with full company context. What took hours/week now takes minutes and gets sharper each time Hyper learns more how he thinks and how his company is changing. Another YC founder one-shotted a launch video script because Hyper already knew their product, voice, positioning accumulated over months.
We have a 3-day free trial, explained more on our pricing page (https://heyhyper.ai/pricing) and there are more details in our FAQ (https://heyhyper.ai/faq), including things like privacy, compliance, and how we’re different from other “memory” companies..
Give it a spin! break it! and tell us where it falls short: https://heyhyper.ai/. We'd love to build you a 10-star experience :) Comments welcome!
Comments
> Facts are the meaning pulled out of each episode, stored as subject-predicate-object records with a plain summary and timestamps for when the fact was introduced and when it was invalidated (subject=person, predicate=works_at, object=company). Facts form a graph with typed edges between them: X is in tension with Y, A is derived from B, J supersedes K.
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!)
It's a good idea to bet on this. There's a lot of business and domain knowledge trapped in random places and mostly aggregated in employees heads. Not very accessible to AI agents currently.
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!
How does your technical approach actually create accurate fact extract?
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.
Congrats on the launch!
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?
Hey!
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!
Pretty cool. I understand the concept, but I wasn't able to get a clear answer on what the app actually does. Is it an MCP server? Or some viewer for my agent-compiled notes? Or just a UI for me to set up integrations? I got to the integrations page, but I'd like to understand what the app does before I just start connecting all my data.
I totally support you guys so don't take it as a dig! But isn't this mindblowing that while you were building and launching, Opus 4.8 launched and made a bunch of things you mentioned above irrelevant? for example, memory between sessions is way better, dynamic workflows will spin up a ton of agents to do work in parallel, and the ecosystem must provide better apis to be relevant (salesforce, uipath goind headless). Again always support startups so cheering for you, but man things are changing so fast!
Nice job! But here is my idea: why not build an agentic AI workflow that mimics the streamlined production methods of Ford in the early 20th century? We already have extremely powerful models and APIs, but we still tend to cram everything into one employee's workstation without giving out different tasks to different people.
How'd you get the license to use "The Jetsons" cartoons?
Interesting product. I know others building in this space. How are things going with existing customers? And how are you measuring deltas vs standard agentic processes? Are you using RAG under the hood?
1. Have you measured the value provided by the knowledge graph layer over straight enterprise search (e.g., https://www.glean.com/) Benchmarks, please.
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?
> The self-driving company brain
Made me think this was for companies working on self-driving.
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Hey HN! Kanyes here, one of the cofounders of Hyper. Here all day to answer any questions :)
Congrats on the launch! Where do you think the core differentiator is right now? I feel like there's a grave yard of Glean for small teams no? Maybe I'm imagining it?