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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
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.
On the analytics side, quan-persona competes within User Experience, Analytics, Artificial Intelligence, GitHub and UX Design — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how quan-persona performed against the three products that launched closest to it on the same day.
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.
For a complete overview of quan-persona including community comment highlights and product details, visit the product overview.
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