Poozle – open-source Plaid for LLMs

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Hi HN, We’re Harshith, Manoj, and Manik Poozle ( https://github.com/poozlehq/poozle ) provides a single API that helps businesses achieve accurate LLM responses by providing real-time customer data from different SAAS tools (e.g Notion, Salesforce, Jira, Shopify, Google Ads etc). Why we built Poozle: As we were talking to more AI companies who need to integrate with their customers’ data we realised managing all SAAS tools data and keeping them up-to-date is a huge infra of ETL, Auth management, Webhooks and many more things before you take it to production. It struck us – why not streamline this process and allow companies to prioritise their core product? How it works: Poozle makes user authorization seamless using our drop-in component (Poozle Link) and handles both API Key and OAuth dance. Post-authentication developers can use our Unified model to fetch data to their LLMs (no need to sync data separately and then normalise at your end). Poozle keeps data updated in real time while giving you options to choose sync intervals. Even if the source doesn’t support webhooks, we’ve got you covered. Currently, we support Unified API for 3 categories - Ticketing, Documentation and Email. You can watch a demo of Poozle ( https://www.loom.com/share/30650e4d1fac41e3a7debc212b1c7c2d)... We just got started a month ago and we’re eager to get feedback and keep building. Let us know what you think in the comments : )

Visit product View on Hacker NewsAug 18, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, user · Missing: mac, agents, macos

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month, google, para · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, shopify · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

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