An authority gate for AI-generated customer communication

Hacker News

Hi HN, As more teams let AI draft or send customer-facing emails (support, billing, renewals), I’ve been noticing a quiet failure mode: AI-generated messages making commitments no one explicitly approved. Refunds implied. Discounts promised. Renewals renegotiated. Not hallucinations but AI doing its job with no authority boundary. I built a small authority gate that sits between AI-generated messages and delivery. It does not generate content or replace CRMs or support tools. It only answers one question before a message is sent=> Is this message allowed to promise money, terms, or actions to a customer? The system inspects outbound messages, detects customer-facing commitments (refunds, billing changes, renewals, cancellations), blocks delivery or requires human approval, logs every decision for auditability I’ve made a public sandbox available for teams experimenting with AI-driven customer communication. I’m not sure yet whether this is a niche edge case or an inevitable new infrastructure layer as AI adoption increases, so I’m especially interested in hearing: a) whether you’ve seen similar failures b) how you’re currently handling authority and approvals or why you think this problem won’t matter in practice Sandbox + docs here: https://authority.bhaviavelayudhan.com Happy to answer technical questions.

Visit product View on Hacker NewsDec 26, 2025

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

77%77% 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: new, email · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% predicted probability of success on TrustMRR, 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

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 · Missing: arr, mrr, revenue

17%17% 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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