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Alceos – AI Knowledge Base for Your Data

Hacker News

Alceos – AI Knowledge Base for Your Data

Hi HN, I wanted to show you Alceos, an interactive knowledge base that makes it easy to get AI to work with your data. Demo: https://youtu.be/EcjxFN_SWbg Basically, you can upload documents and then you have access to all the information of your documents at your fingertips. There is a Q&A tool to answer questions across your entire knowledge base and an AI assistant built-in, that will only rely on your knowledge base for its facts. Think of it like a Bing Chat but that will search the documents you have in your knowledge base instead of the web. Tech details: To build the knowledge base, we use a combination of embeddings, summarizing and pre-processing to allow the ability to have full context of what is in a given document (say 40+ pages) and conditional prompting depending what the document is (ex: an excel file will be processed a bit differently than a word/pdf file). For the AI assistant, it’s a mix a clever prompting, using the Q&A tool when in need of information and “self-reinforcement” / “grounding” to keep it on the rails. In the future, I want to give more tools to the assistant (improved math, task automation, internet access) and an API to make it easy to add/update your knowledge base or integrate knowledge-augmented models into products easily. Also, with LLMs having their Stable Diffusion moment, I think in the next few months we’ll be able to have a self-host version of the product which will be very exciting. I’d love to get some feedback (on the features, pricing, use cases, etc.) or answer any questions you may have! Cheers, Iliane

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, context · Missing: mac, agents, macos
89%89% 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 · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
70%70% 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: host · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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