Stop asking LLMs "is this true?" when they hallucinate, they will just lie again. Verol fixes this by adding an independent verification layer to ChatGPT, Claude, and Gemini. It parses answers, executes real-time web lookups, and validates sources through a dedicated backend pipeline. You get instant verdicts, confidence metrics, and clickable sources. No data tracking; history stays local. 5 free runs to test it. Plans from $4.99/mo.
I built this tool because I got tired of manually copy pasting AI answers into Google one sentence at a time to check if they were real. It was fine for a couple of days, but it quickly became exhausting and I still couldn't trust half of what came back.
Verol fixes this by sitting right on top of the AI chats you already use. Instead of asking the model "are you sure?" (which usually just triggers another confident, hallucinated paragraph), Verol extracts individual claims and passes them through an independent backend pipeline with live web lookups.
It currently works on ChatGPT, Claude, and Gemini. And soon this list will increase. You get a clear trust score, per claim verdicts, and actual, clickable source links. Plus, your verification history never leaves your browser it stays completely local in your browser.
Out of curiosity, I also ran a small benchmark (100 identical prompts across 3 models) to see how they stack up. It’s not a formal scientific study, but the charts are in the gallery if you want to see how often confident-sounding answers fall apart under a live source check.
There's a free tier with 5 verifications so you can test it on your own workflows. I’d love your honest feedback, especially if you rely on LLMs for research, coding, or anything where a subtle hallucination completely ruins your day.
I'll be here to answer questions all day. Thanks for checking it out! 🙏
Independent verification layer is the right framing. Asking an LLM to grade itself is a dead end, so source-backed verdicts and confidence metrics are valuable. How do you handle claims that require paid/logged-in sources or rapidly changing data?
The plausible-sounding hallucinations are the real danger, and they're the hardest to catch because nothing about the answer looks wrong. I work on the data side of this, feeding models pre-verified facts so they don't have to guess, but that only helps when you control the source. For everything else, a verification layer like this is the right shape. The honest "couldn't find a source, here's my confidence" behavior is the part that earns trust. Asking the model to check itself just burns credits and produces another confident paragraph, like Luke said. One question on the real-time mode: verifying every response live sounds great but also expensive in latency and lookups. Do you let people scope it to certain chats or claim types so it isn't running on every throwaway message?
I always find I ask Claude to verify, sometimes multiple times, and it just eats all my credits. This could definitely be very useful
Honestly the part that worries me as a non-coder isn't the obvious hallucinations, it's the plausible-sounding ones I'd never think to question. When I lean on AI to research a post or sanity-check an idea, I'm just... taking its word, because I can't always check the source myself. What does Verol do when a claim isn't really web-checkable, or when the sources it pulls disagree with each other?
This is great! @mark_prod I would love to try this out soon. AI hallucinations has been the bane of my existence lately, just 2 days ago, I was working on a project that required a lot of research, I caught it hallucinating, pointed it out and it immediately changed it's direction. This got me worried if all of my previous research had been wrong and I had to start validating them all. So great job! This is really a problem I'm sure a lot of people face.
I'm curious to know though, what triggers Verol? Does a kind of kinds of question trigger it? Or does it work on the background, validating very response the AI chat gives?
Also, what does it validate against?
About Verol on Product Hunt
“Stop AI hallucinations”
Verol launched on Product Hunt on June 15th, 2026 and earned 86 upvotes and 10 comments, placing #16 on the daily leaderboard. Stop asking LLMs "is this true?" when they hallucinate, they will just lie again. Verol fixes this by adding an independent verification layer to ChatGPT, Claude, and Gemini. It parses answers, executes real-time web lookups, and validates sources through a dedicated backend pipeline. You get instant verdicts, confidence metrics, and clickable sources. No data tracking; history stays local. 5 free runs to test it. Plans from $4.99/mo.
Verol was featured in Chrome Extensions (52.7k followers), Productivity (653.8k followers) and Artificial Intelligence (471k followers) on Product Hunt. Together, these topics include over 252.4k products, making this a competitive space to launch in.
Who hunted Verol?
Verol was hunted by Mark. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Mark
Hey Product Hunt 👋
I'm Mark, creator of Verol.
I built this tool because I got tired of manually copy pasting AI answers into Google one sentence at a time to check if they were real. It was fine for a couple of days, but it quickly became exhausting and I still couldn't trust half of what came back.
Verol fixes this by sitting right on top of the AI chats you already use. Instead of asking the model "are you sure?" (which usually just triggers another confident, hallucinated paragraph), Verol extracts individual claims and passes them through an independent backend pipeline with live web lookups.
It currently works on ChatGPT, Claude, and Gemini. And soon this list will increase. You get a clear trust score, per claim verdicts, and actual, clickable source links. Plus, your verification history never leaves your browser it stays completely local in your browser.
Out of curiosity, I also ran a small benchmark (100 identical prompts across 3 models) to see how they stack up. It’s not a formal scientific study, but the charts are in the gallery if you want to see how often confident-sounding answers fall apart under a live source check.
There's a free tier with 5 verifications so you can test it on your own workflows. I’d love your honest feedback, especially if you rely on LLMs for research, coding, or anything where a subtle hallucination completely ruins your day.
I'll be here to answer questions all day. Thanks for checking it out! 🙏