Freelancers using OpenAI in Islamabad Capital TerritoryFreelancers using OpenAI in Islamabad Capital Territory
ProjectsPeople
Armughan Shahid's avatar
Armughan Shahid
proIslamabad, Pakistan
AI SaaS Dev | LLMs, Agents, Voice & Automation | Web, Mobile
1x
Hired
5.0
Rating
3
Followers
AI SaaS Dev | LLMs, Agents, Voice & Automation | Web, Mobile
Cover image for InForm - AI-Driven Physiotherapy App
InForm - AI-Driven Physiotherapy App for Diagnosis, Rehab & Recovery Tracking The core problem it solves: Patients struggle to get timely, structured physiotherapy guidance, while physiotherapists are overwhelmed managing cases, tracking progress, and creating personalized rehab plans manually. Existing systems either lack intelligence or lack control. This product creates a complete digital workflow where diagnosis, communication, and recovery are all connected into a single system. What was built: A full AI-powered physiotherapy platform with mobile apps for patients and a web-based admin system for physiotherapists. Patients begin by submitting structured symptom data through guided questionnaires. Instead of jumping directly to conclusions, the system uses an AI diagnosis engine to analyze patterns and generate internal clinical suggestions. These suggestions are never shown to patients, they act as decision support for physiotherapists, ensuring every diagnosis remains human-approved. Once a case is created, physiotherapists review patient data, validate or override AI recommendations, and communicate directly through an in-app messaging system. Every interaction is tied to a structured case, ensuring context is never lost. The system then moves into recovery management. Physiotherapists create personalized rehab plans with multi-phase programs, exercise libraries, and video guidance. Patients follow these plans inside the app, logging progress, completing KPI-based milestones, and moving through recovery phases in a structured way. A key part of the system is the progress tracking engine. Patients log metrics, complete phase-based goals, and unlock new stages only when criteria are met. Physiotherapists get real-time visibility into adherence, performance, and patient feedback, making the system both trackable and measurable. Alongside this, an intelligent chatbot handles general queries and reduces load on physiotherapists. When confidence is low or cases become complex, the system escalates conversations into structured cases with full context preserved. Technical architecture: Built as a scalable mobile-first system using React Native for patient apps and React.js for the admin panel, with a Node.js/Nest.js backend and Python powering AI-driven diagnosis logic. Data is managed through PostgreSQL, with secure authentication and encrypted communication layers. The AI layer is designed as a support system, not a replacement. It uses LLM-based reasoning to assist diagnosis and continuously improves through feedback loops based on physiotherapist decisions, creating a human-in-the-loop learning system. Deployed on cloud infrastructure (AWS/GCP), with Stripe integration for subscription-based access and a modular architecture designed for future AI expansion. Business model built in from day one: Subscription-based access for patients, with clear pathways to expand into AI-assisted rehab recommendations, outcome analytics, and protocol optimization. The system is designed to evolve into a data-driven recovery platform where every patient interaction improves future treatment accuracy, turning clinical workflows into scalable intelligence.
0
161
Cover image for AI Operations Agent: RAG-Powered Retail
AI Operations Agent: RAG-Powered Retail Intelligence & Task Automation This project was built for large-scale restaurant groups and multi-unit retail operators who manage high volumes of data across dozens or hundreds of locations. Specifically designed for Regional Managers and Operations Directors, the system serves as an enterprise-grade "Digital Consultant" that bridges the gap between fragmented POS/inventory data and daily on-the-ground execution. By transforming millions of rows of restaurant performance metrics into high-priority tasks, it provides a centralized platform for leadership to monitor KPIs, approve AI-suggested corrective actions, and ensure operational consistency across their entire portfolio. 1. What We Built We developed a production-ready Autonomous AI Operations Agent designed to bridge the gap between complex retail data analysis and daily execution. The system acts as a digital consultant for regional managers, transforming raw KPIs into actionable tasks. Analytical AI Chat: A free-form conversational interface where users can query performance data (e.g., "Show me the top 5 worst profitable stores in Istanbul for the last 3 months"). Task Management Dashboard: A structured workflow where AI-suggested actions are automatically logged for manager approval or rejection. Automated Action Logic: The agent uses an "Action Suggestion Map" to identify specific defects (like low audit scores or high food waste) and suggest precise corrective measures. Persistent Memory: Includes both short-term memory for the current chat session and long-term RAG memory to maintain context over time. 2. How We Built It (The Stack) The system was engineered for scalability and reliability using a modern, containerized stack:AI Orchestration: LangGraph was used to manage complex, multi-turn reasoning and agentic workflows. Frontend: React/Next.js 14 for a responsive, real-time user interface. Backend & Data: Node.js paired with a PostgreSQL database capable of handling 1M+ records. LLM Access: Integrated via OpenRouter to allow for flexible model selection and switching. Infrastructure: Fully Dockerized to ensure consistent deployment across environments. 3. Challenges We Faced As the system scaled from prototype to processing millions of records, we encountered several critical engineering hurdles: Response Latency: The initial monolithic prompt architecture led to response times exceeding 60 seconds, far slower than the required "ChatGPT-like" speed. Prompt Verbosity & Errors: Complex questions involving multiple variables caused the LLM to lose focus, leading to "reasoning errors" and incorrect SQL generation. Hallucination Risks: In multi-branch queries, the model occasionally fabricated data points, particularly around manager hours and performance metrics. Context Switching Bugs: The agent sometimes struggled to "let go" of a previous topic, continuing to reference an old store when the user had asked about a new city. 4. How We Solved It We re-engineered the core pipeline to transition from a single, heavy agent into a Modular Multi-Step Architecture: 75% Latency Reduction: By decomposing the main logic into smaller, task-specific nodes, we dropped processing time from 60s down to 15s. Task Decomposition & Specialized Models: We stopped using a "one-size-fits-all" model. Instead, we implemented a router that uses lighter, specialized models for SQL generation and action identification, and flagship models only for final reasoning. Granular SQL Generation: Breaking the metadata analysis into narrow sub-steps eliminated SQL hallucinations. The model now only "sees" the specific schema needed for the current sub-task, ensuring 100% accuracy. 10-Point Testing Protocol: We implemented a rigorous QA protocol that specifically verified bug fixes for context switching, task duplication, and chart coverage before final delivery.
0
164
Cover image for BudgetNest — AI-Powered Personal Finance
BudgetNest — AI-Powered Personal Finance SaaS Most people don't track their finances because the friction is too high. BudgetNest removes that friction entirely, every transaction captured automatically, categorised intelligently, and surfaced through analytics that actually help people make better decisions. The core problem it solves: Manual expense logging fails because people forget, get lazy, or simply don't have time. BudgetNest built an automated capture layer that works across every channel a user already operates in i.e. SMS alerts, bank emails, receipt photos, WhatsApp messages, and voice notes in English and Urdu. The system deduplicates intelligently across all input sources so nothing gets logged twice regardless of how it came in. What was built: A complete AI finance platform with five distinct automated capture modes SMS and email parsing for bank transaction alerts, PDF and image bank statement upload with AI extraction, OCR receipt scanning via camera, a WhatsApp bot that accepts text, images, and voice notes, and multilingual voice input for manual cash payments. Every transaction flows through an LLM-powered categorisation engine that auto-assigns categories and subcategories, recognises vendors, and learns from behaviour over time. Beyond capture, the system includes smart budgeting with AI-driven suggestions based on spending patterns, subscription detection for recurring transactions, shared expense and split-bill tracking, fraud detection for unusual transactions, and forecasting that projects deficit against income. Dashboards surface everything through charts, trend lines, and weekly and monthly summaries. Technical architecture: React Native across iOS and Android, Node.js and FastAPI backend, PostgreSQL and MongoDB, AWS infrastructure with EC2, S3, and RDS, Python-based NLP and OCR pipeline using Transformers and Tesseract, Twilio WhatsApp integration, Gmail API for email parsing, and Firebase for push notifications. Business model built in from day one: Freemium with premium automation features, B2B white-label capability for microfinance institutions and NGOs, and the OCR and SMS parsing logic architected as standalone APIs for third-party licensing meaning the AI layer has revenue potential independent of the consumer app.
1
191
Cover image for Milematcher - Realtime Competitive Running Platform
Milematcher - Realtime Competitive Running Platform
0
6
OpenAI(3)
Abdul Qadeer's avatar
Abdul Qadeer
proIslamabad, Pakistan
Full-Stack Developer & AI Engineer | Next.js, Flutter
5.0
Rating
19
Followers
Full-Stack Developer & AI Engineer | Next.js, Flutter
Cover image for AI Skincare Analysis App Full Case Study
AI Skincare Analysis App Full Case Study
0
4
Cover image for AI Virtual Try-On Fashion App Case Study
AI Virtual Try-On Fashion App Case Study
0
14
Cover image for AI Calorie Tracking App Full Case Study
AI Calorie Tracking App Full Case Study
0
6
Cover image for Deep AI All-in-One Assistant App Case Study
Deep AI All-in-One Assistant App Case Study
0
4
OpenAI(6)
Shehzada Ammad Ali's avatar
Shehzada Ammad Ali
Islamabad, Pakistan
Full Stack Developer| n8n | Framer| Kajabee| Webflow Expert
1x
Hired
50
Followers
Full Stack Developer| n8n | Framer| Kajabee| Webflow Expert
Cover image for Human Resource Intelligence Chatbot with SQL-Driven Data Vis...
Human Resource Intelligence Chatbot with SQL-Driven Data Visualization I developed an AI-powered HR chatbot that transforms natural language queries into actionable insights through SQL-driven analytics and automated visualizations. How It Works: HR asks questions in plain English AI generates/executes SQL queries Processes workforce data Outputs structured results or charts Key Features: Natural language to SQL conversion Real-time workforce analytics Automated data visualization Scalable integration with HR databases Extendable to other chart types
2
200
Cover image for Rat Paw Detection with YOLOv8 + Gradio This project showcase...
Rat Paw Detection with YOLOv8 + Gradio This project showcases a lightweight and intuitive web interface for detecting rat paws in images using a custom-trained YOLOv8 model. Built with Gradio, it allows users to upload an image and receive instant visual feedback with detected paws highlighted. Key Features: YOLOv8 object detection fine-tuned specifically for rat paw recognition. Interactive web app for image uploads and real-time inference. Built with Gradio for fast prototyping and deployment. Clean interface with automatic annotation overlay.
2
210
Cover image for Contours Extraction & DXF Export from Images-AI-Based End-to...
Contours Extraction & DXF Export from Images-AI-Based End-to-end computer vision solution that automatically detects objects in an image, accurately measures in real-world units, &exports result as a scalable DXF file. Key Features Uses background subtraction & morphological operations, extract objects from image, even in cluttered or noisy backgrounds. Detects known reference object (coin) in image & computes precise scaling factor (mm/px) using OpenCV contours & geometric heuristics. Clean object edges are extracted & converted into vector contours with pixel-level precision. Object outlines are transformed into vector splines & exported as .dxf files, fully compatible with CAD tools like AutoCAD, LibreCAD, SolidWorks, & Fusion360. User-friendly frontend for non-technical users to upload images & receive DXF files .
2
209
Cover image for AI Multlingual voice Chat Application Developed a Multilingu...
AI Multlingual voice Chat Application Developed a Multilingual AI Voice Chat App enabling real-time cross-language communication through AI transcription, translation, and voice cloning. Users exchange voice messages in their native tongues while preserving the sender's vocal identity. How it Works: Set preferred languages Send voice messages AI transcribes & translates Converts text to speech Clones sender's voice
2
137
OpenAI(6)
Talha Ahmed's avatar
Talha Ahmed
proIslamabad, Pakistan
CTO | AI, Three.js, Web, Mobile & SaaS Product Expert
New to Contra
CTO | AI, Three.js, Web, Mobile & SaaS Product Expert
Cover image for 🤖 AI Contract Assistant 📌 Project
🤖 AI Contract Assistant 📌 Project Overview Manual document and contract reviews create massive bottlenecks in corporate compliance and legal workflows. Enterprise teams spend hundreds of hours cross-referencing incoming agreements, submissions, and partner emails against strict organizational rules. To eliminate this friction, I engineered the AI Contract Assistant. This platform allows organizations to define their business rules, policies, and regulatory compliance standards once—and automatically reviews all incoming emails, contracts, and applications in real time. ⚡ Core Workflow One-Time Rules Setup: Organizations upload internal policy documents, safety guidelines, and legal requirements into a secure vector database. Automated Ingestion: The system captures incoming emails, PDFs, Word documents, and application files instantly. AI Rule Validation: The engine cross-references every clause and term against pre-defined rules, generating instant risk reports. Autonomous Counter-Response: If Compliant: The AI automatically progresses the application and communicates with the sender. If Non-Compliant: The AI drafts a precise, itemized report explaining exact violations and required changes. 🛠️ Key Features Custom Rule Engine: Define complex legal, pharmaceutical, financial, or internal company guidelines effortlessly. Smart Multi-Format Parsing: Extracts and processes text from scanned PDFs, .docx files, and raw emails. Autonomous Email Agent: Handles back-and-forth communication without requiring manual oversight. Risk Heatmaps & Dashboards: Real-time visual tracking of compliance status, red flags, and missing clauses. Complete Audit Trail: Full history of every review, rule check, and outgoing response for accountability. 💻 Tech Stack Frontend: Next.js, React, TailwindCSS, TypeScript AI & Orchestration: LangChain, OpenAI GPT-4, LlamaIndex Vector Database: Pinecone (Multi-tenant architecture) Backend: Python, FastAPI, Celery . . . #AIContractAssistant #LegalTech #ArtificialIntelligence #FullStackDeveloper #LangChain #OpenAI #Pinecone #NextJS #PythonDeveloper #FastAPI #AutomatedWorkflows #EnterpriseSaaS #B2BSoftware #ContractAutomation #ComplianceAI #FreelanceDeveloper #ContraHire
0
25
Cover image for AI-Powered Fitness & Wellness Platform Developed
AI-Powered Fitness & Wellness Platform Developed an AI-powered fitness and wellness platform that delivers personalized workout plans, nutrition guidance, and intelligent health coaching through a seamless mobile experience. The application combines AI-driven recommendations with customized fitness programs to help users achieve their health and wellness goals. A standout feature of the platform is its AI Mood Detection, allowing users to interact naturally with an AI coach that understands their emotional state and provides personalized workout recommendations, motivational guidance, and wellness advice based on their mood. The platform also includes nutrition tracking, workout customization, personalized diet plans, progress monitoring, and a 24/7 AI fitness trainer, creating a truly intelligent and engaging fitness experience. Key Features AI Fitness Trainer (24/7) AI Mood Detection & Personalized Coaching Personalized Workout Plans Workout Customization Nutrition Calculator Personalized Diet Plans Goal-Based Training Programs Progress Tracking & Analytics User Authentication & Profiles Responsive & Scalable Mobile Application
0
254
Cover image for LeebAI All-in-One AI Platform (Web
LeebAI All-in-One AI Platform (Web & Mobile) Project Overview: LeebAI is a fully AI-powered, all-in-one platform that brings together the world's top AI models including ChatGPT, Gemini, DeepSeek, Flux, Stability AI, and Claude under a single roof. The platform is available on both web and mobile, designed for users who want to leverage AI for text generation, image creation, coding, research, file analysis, and content summarization all without switching between multiple tools. My Role: Full Stack Developer & AI Engineer Built the entire platform from scratch, including frontend, backend, AI integrations, and mobile application. Key Features Built: Multi-model AI chat interface (ChatGPT, Gemini, DeepSeek, Claude, and more) AI image generation (Flux, Stability AI) Deep research & web research tools File attachment & document analysisYouTube video & website link summarization Code generation & debugging assistant Subscription-based access with multiple pricing tiers Full admin panel for user & subscription management Outcome: The platform was successfully delivered for a startup client acquired via Upwork. LeebAI is currently live and performing exceptionally well, serving users across web and mobile.
0
299
Cover image for Innovative 3D Jewelry Configurator Showcasing Premium Designs
Interactive 3D Jewelry Configurator & Product Showcase Full Description Developed a premium 3D Jewelry Configurator that enables customers to explore and personalize jewelry through an immersive browser-based experience. The platform features real-time 3D visualization, interactive product rotation, metal customization, realistic gemstone rendering, smooth animations, and responsive controls. Built using modern WebGL technologies to deliver a luxury digital showroom that enhances customer engagement and online product presentation. Similar configurators are increasingly used by jewelry brands to let customers preview metals, gemstones, and designs before purchase. Role Senior Full Stack Three.js Developer Work Description Led the end-to-end development of a luxury 3D Jewelry Configurator, creating an immersive digital experience for showcasing premium jewelry collections. Built the interactive frontend using React, Three.js, React Three Fiber, TypeScript, WebGL, and GSAP, enabling photorealistic rendering, real-time material customization, smooth animations, and responsive 3D interactions. Optimized 3D assets and rendering pipelines to deliver a fast, high-quality experience across desktop and mobile while maintaining scalability for future product collections. Technologies React • Three.js • React Three Fiber • TypeScript • WebGL • GSAP • GLTF/GLB • HDR Lighting • PBR Materials • Node.js • REST APIs Key Contributions Built a luxury 3D jewelry experience Real-time metal & material customization Photorealistic gemstone rendering Smooth 360° product interaction Performance optimization for WebGL Responsive cross-device experience
0
142
OpenAI(3)
Bilal Aleem's avatar
Bilal Aleem
Islamabad, Pakistan
AI Automation Expert | n8n | Make .com | LLM Integration
AI Automation Expert | n8n | Make .com | LLM Integration
Cover image for Built ZetaFin — an intelligent
Built ZetaFin — an intelligent financial tracking and reporting system that gives business owners a real-time view of their Profit & Loss without touching a spreadsheet. The system connects to the client's revenue sources (Stripe, PayPal, bank feeds), expense trackers, and invoicing tools, then automatically categorizes every transaction using AI, calculates live margins, flags anomalies, and generates a clean P&L report delivered to the client's inbox every Monday morning. An AI analysis layer adds context to every report — explaining why revenue increased or dropped, which expense categories are trending up, and what actions the owner should consider. Built entirely on n8n with OpenAI for financial analysis and Google Sheets as the live dashboard. The client — an e-commerce founder — went from spending 3 hours every week manually reconciling finances to receiving a fully analyzed, actionable report automatically.
0
10
Cover image for Designed and built Aixen AI
Designed and built Aixen AI a unified communication and lead management platform powered by multiple LLMs working in coordination. The system monitors and manages inbound leads across email, Instagram DM, WhatsApp, and Telegram simultaneously. Each channel has a dedicated AI agent that reads incoming messages, understands context and intent, generates appropriate responses, and escalates or routes leads based on qualification criteria. A central Lead Manager Agent maintains a live unified inbox view deduplicating contacts across channels, tracking conversation history, and scoring lead quality in real-time. Sales-ready leads are pushed to the CRM with a full conversation summary automatically. The client — a digital marketing agency — replaced a 4-person response team with this system, achieving sub-2-minute response times across all channels 24/7. Built on n8n with GPT-4o, Claude, and WhatsApp Business API.
0
10
Cover image for Built Clozr AI a fully
Built Clozr AI a fully autonomous lead acquisition and conversion engine that operates across multiple channels simultaneously. The system scrapes and enriches prospect data from LinkedIn, web directories, and custom sources, then deploys AI-personalized outreach across email, Instagram DM, and cold calling channels all running in parallel without human input. Each channel has its own AI agent handling personalization, follow-up timing, and response classification. A central orchestrator agent monitors performance across channels, auto-reallocates effort toward the highest-converting channel, and escalates hot leads to the sales team via Slack with a full context brief. The client went from manually generating 20 qualified leads per week to 200+ with zero additional headcount. Built entirely on n8n with LLM-powered decision layers.
0
4
Cover image for Built ZetAI — a fully
Built ZetAI — a fully functional AI-powered voice agent that listens to spoken commands and executes real business tasks autonomously. Unlike basic voice assistants that just answer questions, ZetAI takes action: scheduling meetings in Google Calendar, sending emails with AI-drafted content, creating tasks in Notion or Airtable, pulling live data from CRMs, and triggering n8n automation workflows — all from a single voice command. The agent uses a speech-to-intent pipeline: voice input is transcribed, the intent is classified by an LLM, the appropriate tool or workflow is selected, executed, and a voice confirmation is sent back to the user. Supports multi-step commands like "Find my last 3 unpaid invoices, draft a follow-up email for each, and schedule reminders for Friday." Designed as a personal business operating system for founders and executives who want to run their entire operation hands-free.
0
11
OpenAI(3)
Vector xdev's avatar
Vector xdev
Islamabad, Pakistan
AI Software Engineer | Building Production AI SaaS |AI Agent
$1k+
Earned
57x
Hired
4.9
Rating
11
Followers
REPLIT_EXPERT

Expert

AI Software Engineer | Building Production AI SaaS |AI Agent
Cover image for Most AI agents start with
Most AI agents start with a prompt. What if they started with understanding instead? A browser session already contains how work actually gets done: the pages opened the decisions made the information copied the context behind every action Instead of asking people to document workflows or build automations first, imagine software that observes work, understands the business process, identifies gaps, and generates living documentation automatically. Automation comes later. Understanding comes first.
2
48
Cover image for It’s a tool for developers
It’s a tool for developers or power users who want to design sophisticated, multi-step AI interactions through a visual flow chart interface.
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36
Cover image for Run your WordPress site from
Run your WordPress site from Notion pagetowp.com
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36
Cover image for Cursor for images introducing snap2.pro
Cursor for images introducing snap2.pro (https://Snap2pro)
0
40
OpenAI(1)
Mughira Ahmed's avatar
Mughira Ahmed
proIslamabad, Pakistan
Blockchain & AI Engineer | Web3, DeFi, Full-Stack
5.0
Rating
7
Followers
Blockchain & AI Engineer | Web3, DeFi, Full-Stack
Cover image for CardanoGPT - Web3 AI Platform on Cardano Blockchain
CardanoGPT - Web3 AI Platform on Cardano Blockchain
2
3
Cover image for Tap Trade - Local Item Swapping & Bartering App
Tap Trade - Local Item Swapping & Bartering App
1
0
Cover image for FBR Retail POS System
FBR Retail POS System
1
0
Cover image for For decades, finance has relied
For decades, finance has relied on paperwork, intermediaries, and manual processes. Today, programmable finance is changing the equation. Through smart contracts and blockchain technology, financial products can execute automatically, enforce rules transparently, and interact with other systems in real time. The future of finance isn't just digital. It's programmable. #ProgrammableFinance #Blockchain #Web3 #DeFi #SmartContracts #Fintech #DigitalAssets #BlockchainTechnology #FutureOfFinance #MkaitsTechnologies
3
192
OpenAI(1)