Su

Super (YC W18) - Turn company data into answers & agents for your team

Hacker News

Super (YC W18) - Turn company data into answers & agents for your team

Hey there, Chris here We're known for our straightforward yet powerful Knowledge Base, Slite(YCW18).We launched our AI-powered search in Feb 2023 and after getting great response and usage, we dove deeper into solving the challenge of knowledge retrieval in daily work. That's why we're now launching our second major product, Super( https://www.super.work ). Super seamlessly connects your existing tools, providing accurate answers, streamlined workflows, automated digests, and much more. You might wonder: Why not just link your apps together using something like an MCP? The problem is that MCPs can't handle complex knowledge retrieval effectively. MCPs are basically LLMs equipped with API toolbelts. If you've ever tried asking a complicated question through an MCP, one that needs data from multiple different tools, you've likely faced frustrating delays. MCPs slowly make API calls one after another, causing long waits while they collect data from each endpoint. By contrast, Super quickly searches through all the data that actually matters from all of your tools simultaneously. This means you'll get your accurate answer in seconds, not minutes. The limitations of MCP-based solutions become clear when you try to deploy them reliably within a team. They either won't index your critical content effectively, won't do it fast enough, or won't cover all your tools at once. Properly chunking, embedding, querying, and filtering data from various sources is still essential. MCPs triggering APIs can't match this integrated approach for speed and accuracy. Moreover, Super understands the value of running multiple tasks simultaneously through LLMs. For example, one step may involve identifying search filters, while another simultaneously uses an LLM to aggregate and refine information. This parallel process quickly shapes the final, accurate answer for users. Additionally, MCPs aren't designed for enterprise-grade use. Businesses need standardized experiences, fine-grained user permissions, and consistent access controls across multiple tools. Super addresses these requirements by indexing data beforehand while still respecting each user's access permissions. Super offers: - Perplexity-like search experience on your team data - A growing selection of integrations with popular data sources - Customizable AI assistants tailored to your specific needs - An extension to embed Super directly into external websites you're already using - A clear path for your company to adopt AI strategically, rather than letting individual employees scatter across different, incompatible tools. And of course... It does comes with its MCP, which makes your agentic workflows actually able to properly tap on your data. Here's a quick video showing Super in action: https://www.youtube.com/watch?v=L5A6BRW90K4 Have you hit such walls with standard MCPs? Have you try building your own solutions?

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para, compatible · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, video, answers · Missing: mobile apps, ios, personal
62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users, calls · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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