Abdullah Amir's Work | ContraWork by Abdullah Amir
Abdullah Amir

Abdullah Amir

Full-Stack & AI Developer | React Native, Next.js, AI Agents

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Followed by GALLERY L and Zeeshan Z

Islamabad, Pakistan

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Islamabad, Pakistan

Cover image for Not every OnSense user is
Not every OnSense user is on the mobile app — some just want to ask a quick question about their appliance from a browser. Appliance GPT was built as a standalone web product: a focused, consumer-facing chat interface where anyone can describe an issue or ask about error codes, parts, or repairs and get an answer trained on appliance-specific knowledge, without needing to install anything. What I built I built the chat UI solo, using React and Next.js — the conversational interface, message flow, and front-end experience that consumers interact with directly at appliancegpt.onsense.ai (http://appliancegpt.onsense.ai). Stack: Next.js and React for the web chat interface Integration with OnSense's underlying AI/repair-knowledge backend What shipped A live, standalone web app giving consumers direct access to an AI appliance-support assistant — no app download required, just open the browser and ask.
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Cover image for Most people trying to fix
Most people trying to fix a home appliance are stuck flipping through a PDF manual or guessing which part is causing an issue. OnSense set out to replace that with an app where entering a model number instantly surfaces specs, part breakdowns, and an AI assistant trained specifically on appliance repair knowledge — not a generic chatbot. What I worked on As part of the mobile engineering work on the React Native (Expo) app, I built the OCR module that lets users scan an appliance's nameplate with their camera to automatically identify the model and pull it into the system — removing the need to manually type in model numbers that are often worn, tiny, or hard to read. Stack: React Native (Expo) for iOS and Android OCR/camera-based scanning for nameplate recognition AI chat integration (OnSense GPT) for appliance-specific Q&A Backend integration with the platform's parts and repair-knowledge database What's in the app: Model lookup by number, with detailed specs and part breakdowns Exploded View module — an interactive, clickable diagram of appliance parts OnSense GPT chat assistant for error codes, repair steps, and part compatibility Nameplate scanning via OCR for fast, accurate model identification Parts cross-referencing against verified compatibility databases
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Cover image for End-to-end mobile marketplace for buying
End-to-end mobile marketplace for buying and selling pre-loved fashion in Pakistan, built solo from backend to app store release
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Cover image for Onsense AI builds dedicated ML
Onsense AI builds dedicated ML infrastructure for equipment repair — private LLMs and AI agents trained specifically to help technicians, OEMs, and support teams diagnose and resolve repair issues faster. Beyond the core web and mobile products, the platform needed to move from AI as a bolt-on feature to AI agents as a core part of how support, diagnostics, and technician workflows actually run. What I work on I work across the full stack — architecting the platform's Turborepo monorepo, building Next.js web dashboards and NestJS backend APIs, and shipping the React Native (Expo) mobile app for iOS and Android. Alongside that, I'm part of a small team (2-3 engineers) building out the Backend for ERP system, developing frontend for apps and building mobile apps for android and iOS Stack: Next.js and NestJS for web and backend React Native (Expo) for mobile AI integrations PostgreSQL, Redis, Docker across the platform GitLab CI/CD and Playwright/Jest for testing and deployment What shipped A production platform where AI agents aren't a separate feature but a working part of the support and diagnostics experience — backed by a full-stack team handling everything from infrastructure to the agent layer itself.
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