Muhammad Ahmad Mansoor
Engineering intelligent solutions with AI.
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🚀 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
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I just completed a production-ready AI Lead Generation Agent designed to automate how businesses discover, verify, and export qualified leads across multiple data sources.
This system replaces manual lead hunting with an intelligent, filter-driven pipeline that aggregates business data, verifies contact information, and delivers CRM-ready outputs in real time.
🔑 What this agent does:
• Searches businesses by location, industry, and keywords
• Aggregates data from Yellow Pages, Google Maps (API-ready), and extensible sources
• Applies smart filters (company size, founding date, industry relevance)
• Automatically verifies emails, phones, and websites
• Deduplicates leads for clean datasets
• Exports structured CSVs for sales & marketing teams
• Supports real-time queries via a FastAPI backend
• Schedules daily exports and weekly reports
🧠 Tech Stack Highlights:
• Python + FastAPI (async, high-performance backend)
• Selenium-based scraping with anti-bot handling
• Modular lead source orchestration
• Glassmorphism UI with real-time search
• CSV-based persistence (lightweight & scalable)
This project was built with real business use cases in mind — sales pipelines, outreach automation, and scalable lead discovery — not just experimentation.
🎥 Full walkthrough video:
👉
I’m actively building and sharing end-to-end AI systems focused on automation, data intelligence, and real-world impact.
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Building the Future of Hiring: Real-Time AI Interviews
I’m excited to share a demo of our AI-based recruiter automation tool, designed to streamline recruitment funnels with real-time video interviews, deep skill analytics, and cheating detection.
In this video, I demonstrate the full candidate journey:
🔹 Backend Automation: The system extracts skills from my resume and matches them against the job description automatically.
🔹 Interactive Interview: I speak directly with "Higher Vision AI" about my experience in Computer Vision, GenAI, and Agentic AI across industries like security surveillance and e-commerce.
🔹 Technical Deep Dives: We discuss real-world challenges, such as implementing voice-to-voice interaction using LiveKit and how I adapted to new integration hurdles.
🔹 Instant Feedback: The session wraps up with a comprehensive dashboard displaying my interview score, resume score, summary, and skills audit.
I also discuss how I stay ahead of trends using resources like daily.dev (http://daily.dev).
🎧 Note: Please excuse the slight echo on the agent's voice in this screen recording; it is a result of the recording setup, not the live system!
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I wanted to make an AI talk to me like a real human.
Not a chatbot that waits for you to finish speaking. Not a voice note exchange. I wanted an AI that could listen while I spoke, interrupt naturally, and respond instantly — like a personal assistant or customer-care agent. Something that didn’t just reply, but could actually do things — send emails, check dashboards, automate tasks.
So I started small. I used a basic text model with Kokui for speech-to-text. The first time it responded, it felt magical — until it started ignoring me mid-sentence, replying late, or not speaking at all. Latency was awful. It wasn’t a conversation; it was waiting for a robot to remember it existed.
I upgraded to DeepGram, tuned the audio, and still, it felt disconnected. I wanted real-time. I wanted the AI to exist in time with me, not after me.
So I went deep into research. GitHub, StackOverflow, documentation black holes, even different AI platforms — but nothing gave me the real-time link I needed.
Then I found one line buried in a doc:
“OpenAI uses LiveKit for real-time voice.”
That changed everything.
LiveKit works like Zoom — a room where participants join and talk.
I built my agent to join a LiveKit room, then I joined as well.
And for the first time, I wasn’t sending messages to a server — I was talking to an AI inside the same space.
The first test stunned me.
Latency dropped to about 50ms.
The AI listened while I spoke, and responded instantly.
For the first time, it felt alive.
Then came a strange bug.
The AI joined once, but couldn’t reconnect after leaving. I rewrote code, regenerated tokens, nothing worked — until I realized LiveKit doesn’t let you rejoin a room with the same token.
I changed the room name and boom — it worked flawlessly.
Now it runs inside LiveKit Sandbox, talking and listening in real-time.
It can send emails, check dashboards, handle automation — all with almost zero delay.
I started out trying to make a talking AI.
What I built feels more like a digital employee — one that works, listens, and speaks in real time.
And the best part?
It doesn’t feel like the future anymore.
It feels like now.
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