01
Challenge
Matter is a modern read-later app that saves articles, threads, PDFs, newsletters — even YouTube and podcasts, auto-transcribed into time-synced text — into one distraction-free library. The main delivery challenge was to give users dense content and media tools that still feel immediate on everyday devices, while keeping Save, Inbox, and Read consistent across the complete journey. We framed the engagement around the smallest production-worthy flow first, then expanded the scope without weakening the underlying component and data model.
02
How we built it
We began by mapping the user journey and the boundaries of the AI: what context it receives, what it may return, and where deterministic application logic must stay in control. We then built Save, Inbox, and Read as small end-to-end slices, connecting the interface to the data and model layers early so the team could test real responses instead of static mock-ups. Streaming and real-time states were treated as first-class UI states, including loading, partial output, retries, and recovery. The main implementation used React Native, Expo SDK 54, Expo Router, and TypeScript, with typed contracts keeping the JavaScript stack consistent across the product.
03
Delivery
The delivered scope brought Save, Inbox, Read, and Listen into one coherent cross-platform mobile release. Model output, product logic, and user feedback are separated clearly, which makes the AI easier to evaluate and evolve. For Matter, the practical result is a product that communicates its value quickly while giving the team a maintainable base for continued delivery in media & content.
