I built an AI Agent that sell itself

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Quick bit of context: SendRoq is a LinkedIn automation tool. Before the AI era (pre-2021), I ran a marketing agency. Back then, client acquisition on LinkedIn was all manual. Once AI agents started becoming a thing, I built one for my agency's internal use, never intended to sell it. It worked really well for us. A few colleagues and partners kept pushing me to turn it into a SaaS. I said no, we were already profitable running the agency, why complicate things. Then a few weeks later, I was on an onboarding call with a new client, and he asked, "How did you guys know I was looking for marketing help?" I told him straight up: we have an AI agent that analyzes LinkedIn profiles and picks up on signals. If someone comments, engages, or posts anything hinting they're looking for marketing, it flags it and reaches out. He got interested immediately and wanted to buy the agent for his own sales team, was ready to pay well for it. I told him it wasn't public, not for sale. He pushed back: "So make it a SaaS." That conversation is basically what got the ball rolling. A few months later we actually started building it out. The hardest part, unsurprisingly, was getting our first users. Our GTM was pretty meta: we used our own tool to book meetings with potential customers and sell them directly.

Visit product View on Hacker NewsSep 10, 2026

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

91%91% 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: started · Missing: supports, reddit linkedin, podcasting

91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

42%42% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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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