Ask once. GPT-4o, Gemini and Claude answer at the same time — and OneAnswerAI runs a live web search before replying, so the fused answer is grounded in real, current sources instead of outdated training data. Pick the best answer or let it synthesize all three. PDFs, images, 7 languages. Free to try on iOS, no signup.
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Maker
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Hey Product Hunt! Solo dev here, and I'm a little nervous sharing this.
A year ago I caught myself doing something absurd: ChatGPT in one tab, Claude in another, Gemini in a third, pasting the same question into all three and trying to figure out which answer was actually right. So I built an app that does it for me.
OneAnswerAI sends your question to GPT-4o, Gemini and Claude at the same time and fuses the three replies into one answer — either picking the best or synthesizing them.
But the thing I'm most proud of, and the reason I rebuilt half the app this year, is this: it now does a live web search BEFORE answering. Most AI apps (including my first version) just guess from old training data and hand you last year's facts with total confidence — wrong "latest iPhone", outdated prices, last season's news. OneAnswerAI now searches the web in real time, grounds the fused answer in what it actually finds, and shows you the sources. That alone killed most of the hallucinations.
Three things I learned the hard way:
- Fusion can't be summarization. A summary of three answers is worse than any single one — it had to identify what each model got right and resolve the contradictions.
- Knowing when NOT to merge. For a factual/web question, one good grounded answer beats three averaged ones.
- Every query costs me 3-4 API calls, which shaped the entire freemium model.
It's free to try on iOS, no signup. I'd genuinely love your honest feedback — what works, what feels off, what's missing. Solo projects get better because of communities like this one.
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How does the fused answer handle cases where the three models strongly disagree, like with subjective or controversial topics? Do you pick a winner automatically or just surface the conflict?
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Maker
@ceren286877 depends whether they disagree on a fact or an opinion. on facts it tries to resolve it and the web search usually breaks the tie. on genuinely subjective or contested stuff it doesnt fake a single verdict, it lays out the different takes instead of flattening them into one "answer". that second case is the harder one and im still tuning it, but the intent is to show the range, not invent a consensus that isnt there.
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How do you handle conflicting answers between the three models when synthesizing them — is there a rubric for picking what to prioritize, or is it more like a majority vote?
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Maker
@safiyeacrt not a majority vote, three models agreeing doesnt make something true. it keeps the parts they line up on, and where they clash on a fact the live web search settles it instead of whichever model sounds most confident. for numbers theres a separate pass that cross-checks the figures across the models and flags mismatches rather than averaging them. so its reconciled on content, not counted.
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How do you actually decide which model's answer wins when I don't pick one — is it just a confidence score or something more nuanced?
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Maker
@havayigiz45907 no numeric score. the default on the paid tier isnt really "picking a winner", a synthesis step reads all three answers and writes one, keeping what they agree on and reconciling where they differ. anything factual also gets grounded against a live web search, so the merge isnt just three models agreeing with each other. free tier runs a single model so you can test the core first.
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love that it pulls live sources before fusing the answers, that's the smartest guardrail against hallucinations i've seen in a consumer ai app.
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Maker
@ahmetdurma175 thanks, appreciate that. the web search is the piece i care about most honestly, an ai that answers purely from training data will hand you stale info with full confidence, so pulling live sources before the merge is what keeps the answer grounded. still refining when it searches vs answers directly, but its the part im most proud of.
How does the fused answer handle cases where the three models strongly disagree, like with subjective or controversial topics? Do you pick a winner automatically or just surface the conflict?
@ceren286877 depends whether they disagree on a fact or an opinion. on facts it tries to resolve it and the web search usually breaks the tie. on genuinely subjective or contested stuff it doesnt fake a single verdict, it lays out the different takes instead of flattening them into one "answer". that second case is the harder one and im still tuning it, but the intent is to show the range, not invent a consensus that isnt there.
How do you handle conflicting answers between the three models when synthesizing them — is there a rubric for picking what to prioritize, or is it more like a majority vote?
@safiyeacrt not a majority vote, three models agreeing doesnt make something true. it keeps the parts they line up on, and where they clash on a fact the live web search settles it instead of whichever model sounds most confident. for numbers theres a separate pass that cross-checks the figures across the models and flags mismatches rather than averaging them. so its reconciled on content, not counted.
How do you actually decide which model's answer wins when I don't pick one — is it just a confidence score or something more nuanced?
@havayigiz45907 no numeric score. the default on the paid tier isnt really "picking a winner", a synthesis step reads all three answers and writes one, keeping what they agree on and reconciling where they differ. anything factual also gets grounded against a live web search, so the merge isnt just three models agreeing with each other. free tier runs a single model so you can test the core first.
love that it pulls live sources before fusing the answers, that's the smartest guardrail against hallucinations i've seen in a consumer ai app.
@ahmetdurma175 thanks, appreciate that. the web search is the piece i care about most honestly, an ai that answers purely from training data will hand you stale info with full confidence, so pulling live sources before the merge is what keeps the answer grounded. still refining when it searches vs answers directly, but its the part im most proud of.