Shipped an AI-driven mobile chat platform that turns long-form personal context into a continuous wellness conversation
A FlutterFlow-built mobile app for iOS and Android with an AI chatbot at its centre, supported by structured profiling, contextual reminders, networked relationships and a journaling experience, delivered alongside collaborative solution architecture work with the customer’s backend team.
Key Takeaways
A European consumer wellness brand came to Unico Connect to build the mobile app at the centre of their AI-driven wellness product. We delivered a native-feel FlutterFlow application with conversational AI across text and voice, structured profiling across five life domains, contextual reminders and a networked relationships layer.
The engagement included close collaboration with the customer’s backend team on the overall solution architecture, ensuring the mobile experience and the backing services were designed to work together rather than glued together late.
The Challenge
The client was building a wellness product that takes a fundamentally different approach to most apps in the category. Rather than packaging meditation content or habit trackers, they were building a platform where an AI companion learns deeply about the user — across self, love, work, social and parenting contexts — and uses that understanding to support the user through everyday wellness moments. The product proposition is compelling, but the engineering challenge that sits underneath it is substantial.
The team had three engineering needs that all had to be solved together. They needed a mobile app that felt premium across iOS and Android, because a wellness product that relies on emotional context cannot succeed with an interface that feels clunky. They needed an AI conversation surface that handled text and voice equally well, because the value of the conversational AI depends on users actually wanting to talk to it through whatever modality fits their moment. And they needed the conversational experience wired into a deep profiling system across multiple life domains, because the AI’s value depends on the context it has, not just the model it uses.
The client came to Unico Connect specifically for the mobile build, but it became clear during discovery that the mobile build and the backend architecture were too tightly coupled to be developed separately. The conversation extended into how the mobile app, the AI conversation engine and the supporting data layer should fit together, and we worked closely with the customer’s backend team on that overall architecture even though the backend implementation itself remained outside our scope.
Our Approach
We engaged with the client on the mobile build, deliberately structured to include collaborative architecture work alongside the implementation. The first phase was understanding the conversational and profiling experience the product was trying to deliver — working through the AI dialogue states, the profile data model and the way the two interact. A mobile-only engagement that ignored the backend shape would have produced an app that did not connect to the product reality.
Key decisions:
FlutterFlow for speed and a single codebase
FlutterFlow gave one codebase across iOS and Android while compressing time-to-launch, with data-model discipline preserved through the customer’s Xano backend.
Conversation designed as product surface
We spent the most design time on the AI dialogue states, voice playback and the moments the AI guides users back into profiling — treated as product surface, not engineering edge cases.
Dynamic, dependency-aware profiling
Profile questions support free text, binary and multiple-choice formats, with dependencies captured in the data layer so profiling logic can evolve without a new app release.
The solution we built
A FlutterFlow mobile app for iOS and Android with several integrated experiences. The conversational AI is the centre of the product, accessible from the moment the user logs in, and it references the user’s profile state so the conversation and the profile feel like one experience rather than two.
Conversational AI, text and voice
Users converse by text or audio, with native speech-to-text on input and audio playback on output. The AI prompts users back into profiling when context is missing.
Five-domain profiling
Self, love, work, social and parenting domains, each with sub-sections, multiple question formats and dependencies — with a progress indicator showing profile completeness.
Dashboard with reminders
Reminders, suggestions and ongoing discussions gathered into one view, with progressive indicators showing where the user is in their journey.
Networked relationships
Professional, personal and family relationships with bond-status tracking and per-contact chat history, so a conversation about a person retains its context across sessions.
Tech stack
Outcomes & impact
iOS + Android
Mobile app live across both platforms
Text + Voice
AI conversation across both modalities
EN + FR
Languages supported, with centrally managed strings
