Projects using FastAPI in LahoreProjects using FastAPI in Lahore🚀 Built an AI Receptionist for Healthcare — Here's What It Can Do
Managing a healthcare front desk is challenging. Calls, appointment scheduling, patient questions, and administrative tasks often overwhelm staff, leading to long wait times and missed opportunities to deliver a great patient experience.
To address this, I built an AI Receptionist MVP designed specifically for healthcare providers. It can answer calls, schedule appointments, manage inbound and outbound communication, and respond to patient inquiries—all while staying within appropriate clinical boundaries.
Here's what it demonstrated during a live test with our demo clinic, Evergreen Community Health Center:
🧠 Intelligent Patient Conversations
Rather than following a fixed script, the AI understands context and knows its limitations.
✅ Recognized that dermatology wasn't offered and suggested available services instead:
General Practice
Pediatrics
Physical Therapy
Dental Care
✅ Clearly avoided giving medical advice by responding:
"As a receptionist, I'm not qualified to provide medical advice."
✅ Offered the appropriate next steps by either:
Booking a GP appointment for an initial assessment, or
Transferring the caller to clinical staff for medical questions.
📅 Smart Appointment Scheduling
The AI schedules appointments based on both clinic policies and patient preferences.
During the demo, it:
Collected the patient's information.
Suggested the next available appointment.
Adjusted the booking when the patient requested a later time.
Checked clinic operating hours automatically and successfully booked a 5:00 PM appointment before closing.
💳 Administrative Automation
Beyond booking appointments, the AI also handled routine administrative tasks by:
Explaining consultation fees.
Answering policy-related questions.
Confirming referral procedures.
Collecting the patient's email for appointment confirmation.
⚡ Built for Scale
Unlike a traditional reception desk, the AI can handle thousands of conversations simultaneously.
That means:
No busy signals
No waiting queues
No missed calls
Better patient accessibility 24/7
⚙️ Easily Customizable
Every healthcare provider operates differently.
The AI can be configured with:
Clinic-specific services
Staff availability
Business hours
Appointment rules
FAQs
Internal workflows
making it adaptable to virtually any healthcare organization.
This project demonstrates how conversational AI can streamline healthcare operations while allowing staff to spend more time focusing on patient care.
I'm excited to continue expanding its capabilities with integrations such as EMR/EHR systems, multilingual support, voice biometrics, and intelligent call routing.
💬 If you're exploring AI solutions for healthcare, I'd love to connect and discuss how conversational AI can modernize patient engagement.
#AI #ArtificialIntelligence #HealthcareAI #HealthTech #VoiceAI #AIReceptionist #Automation #ConversationalAI #PatientExperience #GenerativeAI #LLM #Innovation 🚀 Building a Graph-Based Fraud Detection System with Neo4j
A single transaction may look normal. Its connections can tell a different story.
I’m building a learning project using Neo4j and Cypher to explore relationships between customers, accounts, transactions, and devices—and flag patterns worth investigating.
🔍 Patterns I’m exploring:
• Multiple customers sharing the same device
• Unusually large transactions
• Rapid transactions within a short period
• Multiple accounts sending money to one account
• Circular transfers between connected accounts
The focus is explainability: showing the relationships behind each flag so the suspicious activity is easier to understand.
Through this project, I’m strengthening my graph data modelling, Cypher queries, and rule-based detection skills.
Next: FastAPI integration, transaction risk scores, fraud alerts, and an interactive monitoring dashboard.
🛠️ Currently a learning prototype, with more updates coming as I build.
What would you investigate first: shared devices or circular transfers?
#Neo4j #GraphDatabase #FraudDetection #Cypher #BackendDevelopment #LearningInPublic Shelfr - AI-Powered Retail Shelf Intelligence Platform
I led the development of Shelfr as the Senior Computer Vision Engineer and Lead Developer, taking the platform from the initial idea and system architecture through development, deployment, and production operations.
My work included:
🔹 Designing the complete computer vision and backend architecture
🔹 Building product detection, shelf analysis, OCR, and image-processing pipelines
🔹 Developing APIs and scalable data-processing workflows
🔹 Deploying and managing production systems on GCP cloud servers
🔹 Optimizing model accuracy, processing speed, and infrastructure performance
🔹 Managing production monitoring, reliability, troubleshooting, and ongoing improvements
🔹 Leading technical decisions across AI, backend, cloud infrastructure, and DevOps
The platform converts real-world retail shelf images into structured product and shelf-level insights, helping automate retail auditing, product visibility analysis, and inventory workflows.
#ComputerVision #RetailAI #LeadDeveloper #AIEngineering #GCP #MLOps #Python #CloudEngineering Building an AI-Powered Platform for Conversations Across Languages
What happens when two people need to communicate but don’t speak the same language?
I am working on an AI-powered multilingual communication platform designed to make conversations easier across language barriers.
The platform allows a user to speak or type a message in their preferred language. It processes the input, translates the message, and returns the result as both readable text and generated audio.
When automated translation is not enough for a complex or sensitive conversation, the experience can also provide access to additional human language support.
My contribution focused on strengthening the technical foundation behind this experience, including:
Voice-to-text processing
Multilingual translation
Text-to-speech generation
Native-script transcription
Backend performance
Concurrent request handling
Web and mobile consistency
Testing and feedback workflows
One of the biggest challenges was ensuring that the product did more than simply list multiple languages as “supported.”
It also needed to:
Process different writing systems
Produce understandable native-script output
Maintain translation and transcription quality
Generate consistent audio responses
Handle multiple language-processing tasks efficiently
Provide a reliable experience across web and mobile devices
I approached the platform as a complete communication system rather than a collection of disconnected AI features.
Translation quality, speech processing, backend architecture, accessibility and human support all needed to work together to create a useful experience.
Over the next few weeks, I’ll share more about how I approached multilingual transcription quality, backend scalability and speech integration while protecting client confidentiality.
This case study contains recreated visuals and anonymized technical information. The client identity, product name, original interface, user information and proprietary workflows have been intentionally excluded.
What do you think is the biggest challenge when building a multilingual product: accuracy, response time or accessibility? Fashion Studio is an AI-powered fashion creation platform designed to help fashion brands and creators generate professional product visuals without traditional photoshoots.
The platform combines virtual try-on, AI model generation, image generation, video generation, face swapping, and quality assurance into a single workflow.
I worked on the backend and AI integration layer, building APIs and workflows that connect the platform with multiple AI services and handle the generation lifecycle from user input to final output.
Key features include:
👗 Virtual Try-On — Apply garments and accessories to generated or uploaded models.
🧑🎨 AI Model Generation — Create realistic fashion models with configurable gender, appearance, measurements, poses, and backgrounds.
🖼️ AI Image Generation — Generate fashion campaigns and product visuals from prompts and references.
🎬 AI Video Generation — Turn generated fashion imagery into promotional videos.
🔄 Face Swap — Generate customized fashion content using face-swapping workflows.
🛡️ AI Quality Assurance — Automatically evaluate generated results and retry failed generations when necessary.
📚 Generation Library — Track and manage generated assets and their parameters.
💳 Credits & Billing — Manage generation credits, subscriptions, usage, and add-on credits.
⚙️ Generation Parameters — Store model configuration, garments, poses, backgrounds, aspect ratios, and generation metadata.
Technology
Python · Django · FastAPI · PostgreSQL · REST APIs · Replicate · OpenAI · Docker · AI Image Generation · AI Video Generation
My Role
Backend Engineer / AI Integration Engineer
I focused on designing the backend APIs, integrating AI generation services, building generation workflows, handling asynchronous processing and failures, and connecting the AI capabilities with the product's user-facing workflows. 
