ToolsAI – AI quotes for blue collar businesses

Hacker News

Hey HN, I built Tols AI over the weekend to help service providers (electricians, landscapers, plumbers, etc.) generate fast, professional job quotes. Most pros type job scopes like: “Replace 200A panel, 3 hours labor, surge protection, bond to code.” ToolsAI takes that and outputs: A clean client-facing quote Labor + materials breakdown Estimated pricing Summary paragraph PDF download It saves time, looks more professional, and helps small contractors close work faster. First 3 quotes are on me :) Would love feedback from builders or anyone who's worked in field services, blue-collar SaaS, or productized workflows. Built with GPT-4o, Flask, and raw frustration from watching people still quote in Notes app or Word templates Thanks!

Visit product View on Hacker NewsMay 26, 2025

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

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: notes, code · Missing: mac, agents, macos

18%18% predicted probability of success on Product Hunt, 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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