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Leadbeam – AI-Powered Field Sales Platform for Reps on the Move

Hacker News

Leadbeam – AI-Powered Field Sales Platform for Reps on the Move

Hey HN, We’re building Leadbeam — an AI-powered field sales platform that helps reps generate leads, plan efficient days, log CRM data, and complete next steps in seconds. The Problem: Field reps still rely on Google Maps, notes apps, and CRMs built for inside sales. As a result: - Hours are wasted on admin tasks and driving from location to location. On average, reps spend less than 28% of their time actually selling. - Over 90% of field activity goes unlogged, leading to low conversion rates and making it nearly impossible to transfer pipelines when a rep leaves or switches territories. - Sales leaders lack visibility into what’s actually happening in the field, making it hard to coach, forecast, or plan territory coverage effectively. What Leadbeam Does: - Update CRM data in seconds using voice, image, and location intelligence - Help prioritize prospects based on rep location and likelihood to close - Generate intelligent follow-ups based on past interactions so no follow-up falls through the cracks - Sales leaders get visibility into real-time rep activity, performance, and territory coverage LeadMaps: Our LeadMaps feature is currently in beta. It helps reps: - Find the hottest leads in their given territory - Optimize routes to maximize daily visits, save time, and fuel - Enrich company data to improve conversion rates Results: - CRM data increased by 6x (some teams 10x) - Reps saved 5+ hours/week - Conversion rates increased by 33% We’re looking for feedback from field sales teams and Sales/RevOps leaders. If you’re curious to see how it works, our mobile app is available on iOS and Android — happy to share links and answer any questions below.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios, maximize · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, activity · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, efficient · Missing: plus, intuitive, reviews
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% 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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