AI agent for first line customer support

Hacker News

I made an AI-powered agent that can handle first-line customer support and I’m really excited about its potential. Narrot aims to provide human-quality support 24/7 at a fraction of the cost. Here's how it works: - Client sends us their customer request via API - Our AI agent analyzes it and responds in seconds - Responses are tailored to your business and data - In case something goes wrong - agent can always call a human (and does that proactively) - Special proprietary protection layers from LLM hallucinations is in place Some stats that blew my mind during development: - Up to 95% reduction in first-line support costs - Response times cut from minutes to seconds - Human agents freed up for more complex issues I know some people worry about ai taking jobs, but honestly, I see this as augmenting human support rather than replacing it entirely. Plus, it lets businesses offer round-the-clock support without burning out staff. There is a free 7-day trial if anyone wants to kick the tires. Would love feedback from you guys!

Visit product View on Hacker NewsOct 16, 2024

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Launch Intel predictions

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

77%77% 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 · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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: excited, ide, io · Missing: https docs, just released, exist

29%29% 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: arr, active · Missing: mrr, revenue, profit

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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