08 / Deployed Systems & Projects
Flirtmetrics — Event-Driven Real-Time Matchmaking & Chat Optimization
CLIENT: Dating Startup | ROLE: Mobile Specialist & Flutter Developer | TIMELINE: 3 Months (2024)
Overview: Optimized a dating app matchmaking page and real-time chat infrastructure. Resolved critical UI thread blockages causing MATCH cards to freeze on swipe, and reduced chat delivery latency from 8 seconds to under 200ms by replacing HTTP short-polling with WebSockets.
The Challenge: Flirtmetrics experienced severe user churn because matchmaking screens froze for up to 1.5 seconds during card swipes (due to synchronous Riverpod state rebuilds of the entire card deck). Additionally, chat delivery suffered from an 8-second delay because the client relied on high-frequency HTTP short-polling, overwhelming network threads and triggering API rate limits.
Solution & Implementation: Designed and deployed a custom WebSocket connection manager in Flutter with heartbeat pinging and auto-reconnect logic. Implemented a local SQLite sync cache, allowing messages to render instantly offline while background threads sync remote state. Rewrote the card swiping widget using isolated state listeners to isolate builds to the swiped card only.
Architecture: Clean architecture implementation using Flutter Presentation layer (Riverpod states), Domain repositories, and Data sources (WebSocket stream client + SQLite offline DB cache). Back-end WebSockets route messages through a lightweight gateway.
Diagnostics: Overcoming CPU thread lockups on low-end mobile devices during card deck recalculations. Bridging dynamic UI transitions with concurrent local SQLite writing and WebSocket message streaming.
Key Metrics: Chat Latency: <200ms (from 8.2s delay) | UI Frame Rate: 60 FPS (stable swiping) | User Retention: +32% (post-release analytics) | App Rating: 4.8★ (iOS & Play Store)
Stack: Flutter, Riverpod, WebSockets, SQLite, Firebase, REST API
Links:[Optimization Report][Play Store][App Store][Website](GitHub: Private Repository (Access available upon request))
ERP Platform — Enterprise Supply Chain & Ledger Performance Engineering
CLIENT: Agro-Industrial Corp | ROLE: Full-Stack Developer & Database Architect | TIMELINE: 5 Months (2025)
Overview: Engineered a highly responsive enterprise resource planning system for stock allocation, accounting audits, and auto-dispatched dealer approvals. Cut down inventory sync errors to near-zero and automated orders workflow.
The Challenge: An agro-industrial firm managing 100+ dealers relied on manual Excel ledgers and paper signature systems. This resulted in an average 24% stock discrepancy between warehouses and a 4-day latency to process orders. Financial reports took minutes to generate because raw SQL queries scanned millions of unindexed records under heavy lock contention.
Solution & Implementation: Built a rule-based state machine in Node.js that routes approval notifications in parallel based on transaction sizes. Implemented compound B-tree indexing in PostgreSQL and partitioned transaction tables by financial quarters. Developed a fast, glassmorphic Next.js dashboard featuring offline service worker synchronization.
Architecture: Next.js App Router for frontend UI, Node.js microservices for state-routing engine, and partitioned PostgreSQL database layer. Push alerts are broadcasted via real-time WebSocket connections.
Diagnostics: Resolving database lock contentions on transaction ledger tables during peak order dispatch periods. Optimizing Next.js client-side memory footprint for dashboards displaying real-time data feeds.
Key Metrics: Stock Error Rate: <0.8% (from 24% discrepancies) | Order Processing: <30m (from 4-day delays) | Approval Speed: Instant (automated workflows) | Sales Velocity: +18% (dealer hub metrics)
Stack: Next.js, React, Node.js, PostgreSQL, Tailwind CSS, Vercel
Links:[Optimization Report][Live Demo](GitHub: Private Repository (Access available upon request))
NEGMP Proposal — National Environmental GIS Monitoring Platform
CLIENT: Ministry of Environment, Forests and Climate Change (Govt. of Bangladesh) | ROLE: System Architect & Proposal Author | TIMELINE: April 2026
Overview: A humble design proposal for a National Environmental GIS Monitoring Platform (NEGMP), submitted to the Ministry of Environment, Forests and Climate Change, Government of Bangladesh. Combining my background in Agriculture with local context from living in the Rajshahi pilot region, I designed a multi-layer monitoring blueprint. I treat this design as an open starting point, eager to adapt and refine it based on feedback from experienced GIS experts and forestry officials.
The Challenge: National plantation monitoring has historically relied on manual field logs, which can lead to reporting gaps. The government sought a way to integrate GPS and satellite-based confirmation. My goal was to design a conceptual path to close this gap, keeping in mind that actual field implementation requires deep listening and adapting to the workflows of local officers.
Solution & Implementation: Designed a conceptual 5-layer architecture centered around a flexible, universal GeoEntity data model. I proposed an offline-first Flutter application to support field workers in low-connectivity areas like the Barind Tract, and a satellite verification pipeline using Google Earth Engine API. I approached this design as a learner, understanding that production-grade remote sensing would rely on collaboration with experienced GIS specialists.
Architecture: Proposed 5-layer system: Backend API (NestJS + PostgreSQL/PostGIS/Prisma), Field Mobile App (Flutter + PWA fallback), Interactive Web Dashboard (Next.js + Leaflet.js / MapLibre GL), Satellite Verification Pipeline (Google Earth Engine API), and Identity/Auth (Keycloak OIDC).
Diagnostics: Designing a flexible data structure that allows future modules (like water level or weather) without complex database redesigns, while recognizing my own learning curve in Google Earth Engine remote sensing workflows.
Key Metrics: Plantation Target: 250M (trees over 5 years) | Pilot Region: Rajshahi (Barind Tract focus) | Verification: NDVI (Google Earth Engine) | Data Model: GeoEntity (universal spatial core)
Stack: NestJS, Next.js, Flutter, PostgreSQL, PostGIS, Google Earth Engine API, Keycloak, Leaflet.js, MapLibre GL, Redis, Prisma
Links:[Optimization Report](GitHub: Proposal Document (Submitted/Under Review))
14 / Machine-Readable Keywords
JA Shuvro, MD. Jonaed Ali Shuvro, Flutter Developer, NestJS, Next.js, Laravel, Node.js, React, React Three Fiber, Three.js, TypeScript, PostgreSQL, MySQL, MongoDB, Redis, WebSockets, System Architecture, Real-time Applications, Full Stack Development, Mobile Development, Enterprise Software, Immigrant Time, Rigg Technologies.