Case study · AI lifestyle app
Zabing
A “Netflix-style” AI concierge for premium bucket lists — shipped investor-ready in a six-week sprint.
6 weeks
to an investor-ready app
52
rotating weekly AI themes per goal
1:1
a dedicated AI chat per bucket-list item
The challenge
Zabing is a premium, AI-powered bucket list platform. The client needed an AI “concierge” that went beyond generic LLM output — a predictive, hyper-personalized content feed, closer to Netflix’s recommendation engine, adapting to each user’s budget, travel frequency, and experience level. On top of that: third-party API integrations (like Unsplash) demanding strict terms-of-service compliance and flawless UI state management.
The approach
We built a custom Cloud Functions pipeline that ingests user “pre-prompts” and “boosters” to generate 52 rotational, highly relevant weekly themes for every user goal — so the feed keeps earning attention week after week instead of repeating itself.
We also rethought the information architecture: standard list items became dedicated “Basecamps” — isolated micro-site hubs, each with a localized AI chat trained exclusively on that bucket-list item’s parameters. And on the frontend we did the unglamorous work that makes an app feel premium: deep state-management fixes, real-time component rebuilds without page refreshes, and secure, ToS-compliant API data handoffs.
The outcome
A highly polished, investor-ready application delivered in a rapid six-week sprint. The AI architecture shifts Zabing’s value proposition from static tracking to continuous, personalized content — the engine behind long-term retention.
Have a similar problem?
If your app needs AI that feels personal instead of generic — and a frontend polished enough to put in front of investors — let’s talk.
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