Asklet widgets ask quick, finely-tuned questions that get better feedback from your customers and users. Let users respond by voice or text, and get the rich detail you need to identify issues faster and easier.
Everyone hates surveys. Users are sick of getting ten-question forms every time they interact with a company, and very few people even bother responding (~6% is typical). Even when they do respond, it's often just a score.
We've come to realise that feedback is only effective when it has detail. I worked for years on employee engagement surveys at Peakon, and realised how powerful verbatim comments are at driving action in companies. I would rather have even just a single description of someone's real experience than any amount of superficial ratings like 8/10.
We built Asklet to drive more effective feedback. It's an embeddable feedback widget that nudges users to add more detail to their ratings. It supports voice and text modes, and can collect detailed feedback in just 45 seconds. It's free for PH users to use, starting today.
It's been so much fun building out Asklet over the past few weeks, I thought I'd share a little more detail on how it works and what's powering it.
Asklet has been built with the same tech we use across the rest of our Surveys platform - Elixir, Phoenix LiveView and Postgres. Why this stack? We've found it incredibly efficient to work with as a small team of 4, and it's built for robust realtime experiences which is exactly the feel we wanted people to have.
For infrastructure we're on Amazon's Elastic Container Service (ECS) which has many of the key elements of full Kubernetes, but with less maintenance overhead. It's a good balance between a fully fledged PaaS, and an entire DIY approach. All the benefits of multi-region scaling, with much less Yaml to wrangle! This is the right fit for us now, but because the application is containerised with few dependencies we can easily move to something else if it makes sense in the future.
The most complex piece was creating a solid experience for users who opted for voice usage. We felt it was important to make this as slick as possible, and allow movement between voice and text modalities for accessibility. After playing around with a few options we settled on a WebRTC connection to OpenAI's Realtime API - this is primarily designed for telephony like products so we spent most of our time tweaking the integration to get it just right for what we needed.
Some of the other challenges we dedicated time to were:
Finding the balance between digging deeper for the best possible feedback, and not creating a tedious or frustrating experience for people who were responding.
Supporting a preview experience for those building an Asklet, as well as a standalone and embeddable version for respondents.
At some point we'll put up a more detailed explanation of how it all works, but feel free to drop any questions. We're really happy to share!
Having worked in the UX & Tech industry for many years, I know how valuable this could be!
Researchers know that user feedback should be as natural and authentic as possible, and given of their own volition. However there's nothing natural about what we do currently with feedback widgets—contriving all their nuanced and rich feedback into a simple score out of 10, or thumbs up.
My hope for Asklet is that it helps us bridge that gap between collecting feedback at scale and collecting much more nuanced, detailed, and human responses that can be genuinely useful in measuring success and making improvements to the customer experience.
Excited to see where it goes! ☺️ 🚀
Very excited about this! I'm so tired of useless, stale surveys, both when I'm asked to fill one out and when reading the generic analysis that goes on top of the dataset. Voice is the new UI!
About Asklet on Product Hunt
“AI-powered NPS widgets that collect natural voice feedback”
Asklet launched on Product Hunt on November 11th, 2025 and earned 129 upvotes and 9 comments, placing #22 on the daily leaderboard. Asklet widgets ask quick, finely-tuned questions that get better feedback from your customers and users. Let users respond by voice or text, and get the rich detail you need to identify issues faster and easier.
Asklet was featured in SaaS (42.1k followers) and Artificial Intelligence (468.9k followers) on Product Hunt. Together, these topics include over 137.4k products, making this a competitive space to launch in.
Who hunted Asklet?
Asklet was hunted by Joe Cainey. 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.
Want to see how Asklet stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Joe Cainey