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rtrvr.ai – AI Web Agent for Automating Workflows and Data Extraction

Hacker News

rtrvr.ai – AI Web Agent for Automating Workflows and Data Extraction

Hey HN, I'm excited to share rtrvr.ai, a Chrome extension that brings the power of AI agents to your everyday web browsing. It's designed to automate complex web tasks, extract structured data from any website, and integrate with your favorite tools as you browse using AI Function Calling [ie: “Send this page summary as Slack message”]. The core idea is to let anyone, even non-developers, leverage the power of web automation and data extraction using natural language. Imagine being able to: Automate lead generation: Extract hundreds of LinkedIn profiles to Google Sheets, complete with AI-generated, personalized intro emails. Process PDFs in bulk: Pull data like revenue, expenses, and totals from hundreds of local or online PDFs directly into your spreadsheets. Navigate and extract from paginated lists: Tell rtrvr.ai, "For each YC Partner, go to their LinkedIn profile and retrieve their name, headline, job, and college," and it'll do it. Automate workflows across multiple tabs: For example, fill out job applications on Careers tab using information from the LinkedIn tab. Function Calling: Integrate with APIs like Snowflake and Slack directly from your prompts using simple @ notation or letting the AI infer what tool to use with natural language. GraphBot: Generate charts and visualizations from website data with natural language commands. Recordings: Ground the agent with site interactions recordings to ensure accurate and repeatable task execution. Sheet Context: Use Google Sheets data as context for your web tasks. Scheduling: Run automations on a schedule in the background. Sheets Workflow: You can feed a Google Sheet with a column of URLs (like LinkedIn profiles), and it will open each url as a tab, extract data, and even generate content (like intro emails) back into the sheet. It can handle multi-step workflows with prior output dependencies, effectively representing a DAG. I see rtrvr.ai as a step towards a more intelligent and interactive web. I believe this tool can be a game-changer for marketers, sales professionals, SMBs, and anyone who needs to extract information from the web efficiently. Would love to hear your feedback, suggestions, and any use cases you can think of! Website: rtrvr.ai Chrome Store Listing: https://chromewebstore.google.com/detail/rtrvrai/jldogdgepmc... Youtube Intro: https://www.youtube.com/watch?v=wajCM6208cc

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, slack · Missing: mac, macos, cursor
95%95% 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: efficiently · Missing: supports, reddit linkedin, podcasting
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
61%61% 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: personal, google · Missing: mobile apps, ios, entrepreneurs
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, active · Missing: arr, mrr, profit
14%14% 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.

Correct prediction on native model

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