An agent for live video sales calls – Fluidworks

Hacker News

At Fluidworks, we're building an AI agent to conduct live video calls for sales demos and onboarding sessions. Presently, our agent can: - Engage with customers during live video sales calls - Follow scripts crafted by the sales or onboarding teams - Demo software functionality in real time, interactively - Address inquiries and provide demos - Present slides based on queries (currently static, with plans for dynamic integration) Our roadmap includes: - Integration as a Zoom/Meet app - Enhancing demos with studio-like cuts Something interesting we figured along the way is that sales teams prefer AI agents to skip small talk, but customers appreciate it. Adding small talk has increased customer engagement by almost 60%. Despite a small sample size of approximately 75 live calls, with 40 including small talk, it's an intriguing observation. Explore our demo videos showcasing the AI agent trained on Salesforce: https://youtu.be/HCESj7kywbw We’d love more feedback from anyone who might be interested in our tool.

Visit product View on Hacker NewsMar 28, 2024

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

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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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 · Strong signals: active · Missing: arr, mrr, revenue

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

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

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