Flutter App Delivery
Business screens, state management, API integration, media upload, edge states, and Android/iOS/OHOS release adaptation.
Jacky Chou
12-Yr Mobile Architect / Flutter Tri-Platform / Full-Stack AI Agent Engineer
With 12 years of Android native and mobile engineering experience, I have delivered two commercial brands across Android, iOS, and OpenHarmony to app stores, and independently ship full-stack workflows from UI design and Java 21 backends to RAG/MCP and multimodal write-back agents.
I focus on high-assurance, verifiable delivery: production mobile apps, native runtimes, RBAC backend APIs, controlled AI boundaries, pre-registered evaluations, and real-device proof.
12 yrs
Android native foundation & commercial app delivery
2 Brands × 3 OS
Shipped across Android, iOS, and OpenHarmony stores
AI Full-Stack
UI → Java 21 Backend → App → RAG / MCP / Write-Back Agent
Capability Matrix
A practical stack for shipping client-ready products: mobile experience, native capability integration, backend systems, AI boundaries, and delivery evidence.
Business screens, state management, API integration, media upload, edge states, and Android/iOS/OHOS release adaptation.
Permissions, camera, location, QR scanning, push, sharing, WebView/MethodChannel, Gradle builds, and real-device debugging.
RAG, embeddings, pgvector, OpenAI-compatible providers, local models, MCP tools, Agent workflows, and human handoff boundaries.
Selected Work
The selected cases emphasize responsibility, architecture, AI control boundaries, native app capabilities, and validation signals.
Primary Flutter Case
Flutter / Android / iOS / OpenHarmony
Delivered two commercial real-estate brands across Android, iOS, and OpenHarmony via Flutter + native integration, all published on mainstream app stores.
2 commercial brands shipped across Android, iOS & OHOS stores
Mainstream B2B app & IM capability coverage
Native Android/iOS/OHOS capability integration
AI Workflow
Flutter + Vue + Spring Boot + RAG
An AI-first support workflow where knowledge-base answers are attempted first and low-confidence or out-of-scope questions move into human tickets.
Controlled AI response boundaries
Human handoff and audit trail
Reusable MVP pattern for small businesses
AI Data Product
Flutter + Spring Boot + LLM
A data-to-report workflow that uploads CSV/Excel files, computes metrics asynchronously, and generates reviewable Chinese business reports.
Backend-owned metric calculation
Reviewable AI narrative generation
OpenAI-compatible and local model validation
Knowledge AI
Java 21 / Spring Boot + Python LangGraph + MCP
SQL-level ACL retrieval, backend citation verification, LangGraph multi-step decomposition (6/12 → 12/12), revocation enforcement (0/3 leakage), and official MCP SDK server.
0/3 post-revocation leakage & 12/12 cross-doc accuracy
Backend-verified citations & fail-closed handoff
182 Java tests + standalone Python MCP server
Multimodal Write-Back Agent
Python + Dual-VLM + Spring Boot Write-Back
Extracts repair, maintenance, and acceptance forms (PDF, DOCX, scans, phone photos) with high-res second-look validation, deterministic table rules, and idempotent work-order write-back.
0 wrong auto-writes on 20 held-out docs & 20 real photos
0 duplicate or lost work orders under fault injection
Pre-registered evaluation discipline + 217 automated tests
Java Full-stack
Spring Boot + Vue Admin
A reusable operations platform base covering authentication, RBAC, audit trails, customers, tickets, approvals, and smoke validation.
Reusable backend starter pattern
Admin workflow and API integration
Docker and smoke-test evidence
Proof Evidence
The portfolio is structured around evidence: delivery scope, architecture, validation, AI boundaries, and communication-ready project narratives.
Case studies include screenshots or productized visuals, architecture nodes, validation evidence, and role boundaries.
AI cases emphasize source citations, out-of-scope handling, human handoff, and replaceable model providers.
Mobile apps, Java backends, Vue admin systems, RAG, Agent workflows, deployment, and verification are presented as one delivery system.
Core case-study repositories are kept private to protect project context, but code, commit history, build logs, and runtime evidence can be shared through authorized review when needed.
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