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quan-persona
Bridge enterprise service design with AI analytics
An AI-empowered, human-centred research and analytics Platform designed to bridge complex insurance agent behaviours with enterprise-grade service design maturity models.
I’m the maker behind quan-persona. As a service designer in complex enterprise ecosystems, I noticed a persistent gap: digital transformations often apply AI only at isolated stages, leaving dynamic user journeys unoptimised. Meanwhile, traditional tools rely on rigid, static personas built on human bias rather than empirical reality.
I built quan-persona, a case study using insurance sales agents, to explore that gap. It pairs a structured questionnaire with unsupervised machine learning (DBSCAN to flag outliers, K-Means to group respondents), so behaviour clusters and sales-journey pain points emerge from the response data rather than from predefined personas. Everything runs in your browser, with no backend, so your survey data is never sent to a server.
What you can do with it:
Test the live interactive dashboard (the demo dataset is synthetic)
Upload your own survey CSV, analysed locally in your browser
Explore research-derived personas and sales-journey pain points
Apply Human-in-the-Loop frameworks aligned with AI maturity models
Review the MIT-licensed code on GitHub, so you can verify the no-backend claim yourself
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About quan-persona on Product Hunt
“Bridge enterprise service design with AI analytics”
quan-persona was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #151 on the daily leaderboard. An AI-empowered, human-centred research and analytics Platform designed to bridge complex insurance agent behaviours with enterprise-grade service design maturity models.
quan-persona was featured in User Experience (369.2k followers), Analytics (174.2k followers), Artificial Intelligence (481.6k followers), GitHub (41.4k followers) and UX Design (7.1k followers) on Product Hunt. Together, these topics include over 216.4k products, making this a competitive space to launch in.
Who hunted quan-persona?
quan-persona was hunted by Jennie Lai. 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 quan-persona stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Jennie Lai
Hi Product Hunt! 👋
I’m the maker behind quan-persona.
As a service designer in complex enterprise ecosystems, I noticed a persistent gap: digital transformations often apply AI only at isolated stages, leaving dynamic user journeys unoptimised. Meanwhile, traditional tools rely on rigid, static personas built on human bias rather than empirical reality.
I built quan-persona, a case study using insurance sales agents, to explore that gap. It pairs a structured questionnaire with unsupervised machine learning (DBSCAN to flag outliers, K-Means to group respondents), so behaviour clusters and sales-journey pain points emerge from the response data rather than from predefined personas. Everything runs in your browser, with no backend, so your survey data is never sent to a server.
What you can do with it:
Test the live interactive dashboard (the demo dataset is synthetic)
Upload your own survey CSV, analysed locally in your browser
Explore research-derived personas and sales-journey pain points
Apply Human-in-the-Loop frameworks aligned with AI maturity models
Review the MIT-licensed code on GitHub, so you can verify the no-backend claim yourself