Zams – Build AI agents that automate sales work

Hacker News

Hey HN, We just launched Zams — a platform to build AI agents that automate repetitive B2B sales tasks like CRM updates, deal follow-ups, lead research, and forecasting. You describe what you want in plain English, and your agent gets it done across tools like Slack, Notion, HubSpot, Salesforce, and more. Zams agents run in two ways: - Command agents – Triggered on-demand from Slack or the web - Ambient agents – Run in the background on a schedule, using context from docs, emails, and dashboards No brittle workflows, no “if-this-then-that” builders. Just intelligent agents that act. We started building Zams 60 days ago. It’s now live, running across 100+ apps, and already in use by dozens of sales teams. If you’re curious about agentic automation or tired of duct-taping tools together, we’d love your thoughts and feedback. Upvote us on PH: https://www.producthunt.com/posts/zams-2 Best, Nirman Dave

Visit product View on Hacker NewsJul 23, 2025

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

98%98% 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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

28%28% 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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