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Askmore – Let an AI run user interviews on your behalf

Hacker News

Askmore – Let an AI run user interviews on your behalf

Hi HN, We're Arindam and Julien and we're building AskMore.ai. AskMore uses an AI to run user interviews on your behalf. We like to think about it as a new user research method that sits between traditional user interviews and user surveys: It's as easy as sending a survey link but the AI will adapt to the user answers, follow up and dig deeper to provide you with insights as deep as what you'd get in a user interview. We got the idea after a lot of struggles to get feedback on our other side projects. We couldn't get many user interviews (low response rate, long delays to schedule calls), and surveys were providing answers that were too shallow. We have a few people running interviews on AskMore and the main benefits we're seeing are: - You can get feedback way faster than if you had to schedule calls (for instance we got more than 40 answers to our own interview in just a few days). - It works in any language so you can interview users you couldn't hear before. - It's more lightweight for the participant so you get a better response rate and it can even be a good first step that can unlock a call. - The AI follows user research best practices (we've been particularly inspired by The Mom Test, but you can override everything) so you don't need to know how to ask the right questions. In fact, I've been amazed multiple times by how the AI would adapt to the conversation and find insights I wouldn't have been able to find myself if I had been conducting the interview. Something important is that we don't think this should replace traditional interviews. It's still super important to connect directly with your users. But this could be a good complement to get more feedback, or to help making the first step. It's super early and rough, but it works and we're eager to get more feedback and to see how it behaves on more use cases. Feel free to give it a try (message us on our Intercom, we can provide more free credits) or just share your thoughts on the concept. Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
93%93% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: answers, users, way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users, calls · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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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