Jacky Chou

Flutter / Android / Java / AI Engineering

2015-now / Mobile / Backend / AI

Building mobile business systems, Java backends, and practical AI workflows

Since 2015, I have delivered production-facing mobile apps and business systems across Flutter, Android, Java backends, admin platforms, RAG knowledge bases, and MCP/Agent workflows.

I focus on complete delivery: product flows, native capabilities, backend APIs, AI boundaries, real-device verification, and deployment evidence.

11+ yrs

App and business system delivery since 2015

Full-stack

Flutter, Android, Java backend, Vue admin

AI-ready

RAG, MCP, Agent workflows, local LLM validation

Capability Matrix

Mobile, backend, and AI delivery

A practical stack for shipping client-ready products: mobile experience, native capability integration, backend systems, AI boundaries, and delivery evidence.

FL

Flutter App Delivery

Business screens, state management, API integration, media upload, edge states, and Android/iOS/OHOS release adaptation.

FlutterDartRiverpod
AN

Android Native Capabilities

Permissions, camera, location, QR scanning, push, sharing, WebView/MethodChannel, Gradle builds, and real-device debugging.

JavaKotlinGradle
AI

AI Engineering

RAG, embeddings, pgvector, OpenAI-compatible providers, local models, MCP tools, Agent workflows, and human handoff boundaries.

RAGMCPAgent

Selected Work

Case studies with delivery evidence

The selected cases emphasize responsibility, architecture, AI control boundaries, native app capabilities, and validation signals.

Primary Flutter Case

Flutter / Android / iOS / OpenHarmony

Pocket Home Flutter

A multi-platform B2B Flutter app covering property listings, customers, workbench, messages, profile flows, native capabilities, and release verification.

FlutterAndroidiOSOHOS

Real-device screenshots and release evidence

Mainstream app capability coverage

Native Android capability integration

Chinese case detail

AI Workflow

Flutter + Vue + Spring Boot + RAG

AI Customer Service Ticket System

An AI-first support workflow where knowledge-base answers are attempted first and low-confidence or out-of-scope questions move into human tickets.

RAGSpring BootVue

Controlled AI response boundaries

Human handoff and audit trail

Reusable MVP pattern for small businesses

Chinese case detail

Knowledge AI

Spring Boot + pgvector

RAG Knowledge Base

Document upload, chunking, embeddings, Top-K retrieval, source citation, and out-of-scope handling for enterprise knowledge Q&A.

RAGpgvectorEmbedding

Source-backed answers

Out-of-scope protection

Reusable enterprise Q&A base

Chinese case detail

Contact

Open to mobile apps, backend systems, and AI MVP delivery.

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