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Defeating IVRs (i.e., phone trees) with a semantic cache

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Defeating IVRs (i.e., phone trees) with a semantic cache

Do you or your application have to frequently complete an Interactive Voice Response (IVR) at the beginning of a phone call? It turns out you can use semantic caching to navigate past an IVR. In addition, you can use semantic caching with templating to input data into an IVR and extract data from an IVR. I know, IVR is different than a phone tree. But most people think an IVR is a phone tree when they interact with it. Would love to hear what the HN community thinks!

Visit productView on Hacker NewsJun 18, 2024
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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
73%73% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
60%60% 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 · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
20%20% 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
8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

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