Work

Engineering case studies

Not a portfolio of demos. These are production mobile products built for real users — shipped to the App Store and Play Store, with real constraints around performance, security, and reliability.

Production React NativeAI-powered experiencesSecurity-first design

Status: Active

Currently building

FlagshipActive2026

SentinelRN

Runtime Integrity & AI Security SDK · React Native

An open-source security SDK that answers the question most mobile security libraries leave unanswered: "can I trust this environment enough to perform this action?" It detects compromised runtimes (root, jailbreak, emulators, hooking, tampering) and inspects AI-bound input for secrets, PII, and prompt injection — returning structured, explainable threat reports instead of bare booleans. Built end-to-end as creator and sole engineer.

What I owned

· Trust core: risk scoring & policy engine· AI subsystem: secret/PII/injection detection

Engineering focus

· One noisy-OR risk engine for every signal· Fail-open — a detector crash is never an app crash

88unit tests

30+detection rules

3published pkgs

TypeScriptReact NativeKotlinSwiftTurborepopnpmVitestBiome

Status: Shipped

Delivered work

FlagshipShipped2026

NestleAI

AI-Powered Parenting Companion · iOS & Android

A production React Native (Expo) app: an AI 'co-parent' for new parents. Offline-first baby-care tracking wrapped around a personality-driven AI companion — shipped to both app stores. Built end-to-end as the founding engineer.

What I owned

· End-to-end mobile app (iOS + Android)· AI chat UX & personality system

Engineering focus

· Offline-first SQLite + durable sync queue· New Architecture (Fabric) for 60fps UI

60 fpsNew Arch UI

Offlinedata layer

iOS + Androidapp stores

React NativeExpo SDK 54TypeScriptZustandSQLiteClerkRevenueCat

Shipped2026

NestleAI — Backend & AI Platform

AI & Backend Platform · Node.js

The intelligence layer behind the NestleAI app: a modular Node.js/TypeScript backend powering real-time AI conversations, safety guardrails, 15+ background worker queues, and a layered AI pipeline — built safety-first for an infant-care domain.

What I owned

· AI pipeline & personality engine· Safety doctrine & guardrails

Engineering focus

· Modular Express monolith, extraction-ready· BullMQ + Redis worker fan-out (25+ workers)

<180msP95 Latency

99.97%Uptime

<1.2sAI Response

Node.jsTypeScriptExpressPostgreSQLRedisBullMQOpenAICloudflare R2

FlagshipShipped2026

Media Learning App

Offline-First Audio/Video Platform · iOS & Android

A mature cross-platform learning app that streams long-form audio and video, downloads for offline playback, and resumes where you left off. I led a ground-up rewrite of the playback engine for offline-first reliability and a major framework upgrade of the underlying codebase — anonymized engineering case study.

What I owned

· Ground-up audio playback engine rewrite· Offline-first downloads & playback

Engineering focus

· Offline-aware media source selection· Modular playback subsystem (~15 modules)

~15playback modules

~80deps aligned

iOS + Androidplatforms

React NativeExpo SDK 55TypeScriptApollo GraphQLTrack PlayerReanimatedFlashListEAS + OTA