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AgentCheck Free Mini-Check
We audit your AI chatbot before it costs you customers
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We audit your AI chatbot before it costs you customers
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Your chatbot talks to customers while you sleep — but do you know what it's actually saying? AgentCheck is a security & privacy audit for small-business AI chatbots. We test what your bot leaks (keys, internal data, system prompts), what it promises (discounts and guarantees you never approved), and where it invents prices and policies. Start with a free mini-check. The full audit ($149 / $299 deep) is human-reviewed: a plain-English report with severity ratings and exact fixes.
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Hi Product Hunt 👋 I'm the maker behind AgentCheck, from CyberShield in Portland, OR.
We kept seeing the same story: a small business adds an AI chatbot to its website, and nobody ever checks what that bot actually does. So we checked. We scanned 100 small-business websites with chatbots, looking only at what's publicly visible:
• 66 of 100 had their bot configuration visible publicly, at least in part
• 20 of 100 had no HSTS (a basic HTTPS protection)
• 5 of 100 had no privacy policy link anywhere near the bot collecting customer data
• 0 of 100 leaked a real secret key in public code — and telling "public by design" apart from "actually dangerous" is exactly what an audit is for
AgentCheck is that audit. It starts with a **free mini-check**: paste your site URL and get the public-surface findings in minutes. The **full audit** goes further — we converse with your bot the way a manipulative customer would: Does it leak its instructions or internal data? Invent discounts, refunds or guarantees? Hand out wrong prices and policies with total confidence? You get a plain-English report: every finding with a severity rating and the exact fix.
Pricing is one-time: **$149** standard audit, **$299** deep audit (more attack rounds + a re-test after your fixes). Optional Guard monitoring is $49/month. No subscription required for the audit itself.
If you run a chatbot on your business site, run the free check and tell us what surprised you. Honest feedback — especially "this finding was wrong" — is what we're here for. Happy to answer anything about the method.