Anas Kaithakoden
AI Automation Systems for Real Business Workflows
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Built an AI-powered voice receptionist system capable of handling real-time phone conversations, answering customer questions, and assisting with appointment booking workflows.
The system combines speech-to-text, large language models, and text-to-speech technologies to create natural voice interactions over phone calls.
Key contributions:
• Built a real-time voice conversation workflow for handling inbound customer calls
• Integrated speech-to-text processing for live voice transcription
• Used AI language models to generate context-aware conversational responses
• Implemented text-to-speech voice generation for natural AI responses during calls
• Developed multi-turn conversation flows for collecting customer information and appointment details
• Built backend logic to manage conversation state and call workflows
• Worked on browser-based softphone integration for handling calls through the web
Tech stack:
• Python
• Flask
• Twilio
• Whisper
• Groq / Llama 3
• ElevenLabs / Edge-TTS
• AI Conversation Systems
• Voice Automation
This project strengthened my experience in voice AI systems, conversational workflows, speech processing, and real-time automation for customer communication.
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Developed a data extraction system using Python and Selenium to collect and organize structured business data from web sources.
The project focused on automating large-scale restaurant data collection while handling dynamic website content and multi-page navigation reliably.
Key contributions:
• Built automated scraping workflows using Python and Selenium
• Extracted restaurant and business data from multiple pages and dynamic web elements
• Handled pagination and browser interactions for reliable large-scale data collection
• Cleaned, organized, and structured extracted data into CSV format for analysis and business use
• Improved efficiency by automating repetitive manual data collection tasks
Tech stack:
• Python
• Selenium
• CSV Data Processing
• Web Scraping
• Browser Automation
Result:
Delivered organized datasets ready for analysis, reporting, and operational use.
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28
Built a fully automated multi-platform content publishing system that transforms a single Telegram video into ready-to-publish content across multiple social media platforms.
The system automates the entire workflow — from receiving a video through Telegram to generating captions, processing content with AI, and automatically uploading posts to platforms like YouTube, Instagram, X, and Reddit.
One of the key features was creating a complete review and approval flow directly inside Telegram, allowing users to approve or manage content before publishing without needing separate dashboards or tools.
Key contributions:
• Built an end-to-end automated content pipeline using Python and workflow automation tools
• Integrated Whisper for speech-to-text transcription with OCR fallback support for extracting text from videos when needed
• Used local LLMs with Ollama and Llama 3 to generate captions and social media content automatically
• Automated browser-based uploads across multiple platforms using Playwright
• Developed Telegram-based controls for content review, approval, and publishing workflows
• Designed the system to run fully locally without relying on paid AI APIs
Tech stack:
• Python
• Playwright
• Whisper
• Ollama
• Llama 3
• Telegram Bot API
• OCR Processing
• Browser Automation
• AI Content Pipelines
This project strengthened my experience in AI-powered automation, browser scripting, local AI workflows, and scalable multi-platform content automation systems.
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Built and implemented automation systems focused on streamlining lead management, customer communication, and sales workflows for businesses using AI and workflow automation tools.
Key contributions:
• Designed and automated complete lead pipelines using n8n workflows
• Integrated Meta Ads lead data into centralized systems for lead tracking, organization, and follow-up automation
• Built AI-powered WhatsApp assistants capable of handling instant customer responses, answering common questions, and maintaining natural conversations
• Developed discovery call assistants to qualify leads, collect customer information, and reduce manual pre-sales work before human sales calls
• Automated repetitive operational tasks across lead handling, messaging, notifications, and follow-up processes
• Integrated APIs and messaging platforms to create seamless communication flows between customers and internal systems
• Improved response times and reduced delays in customer communication through automated workflows and AI-assisted interactions
Tech stack:
• n8n
• WhatsApp Business API
• Meta Ads Integration
• Python
• REST APIs
• AI Assistants / Chatbots
• Workflow Automation Systems
This work focused on helping businesses reduce manual effort, organize lead data more effectively, and create faster, more scalable customer communication systems.
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I built a WhatsApp AI Assistant using n8n and the Meta Business API to automate customer communication, appointment booking, and follow-ups directly inside WhatsApp.
The assistant is designed to handle natural conversations with users, answer questions, manage bookings through Phorest, and send automated appointment reminders — all through a fully automated workflow.
One of the key challenges was that Meta does not provide persistent chat history storage. To solve this, I built a custom chat history system with a WhatsApp-style interface that recreates the native messaging experience while keeping all conversation data fully controlled and accessible.
The system combines AI-driven conversations with workflow automation to create a smooth customer support and booking experience.
Features:
• AI-powered WhatsApp conversations
• Appointment booking integration with Phorest
• Automated reminders and follow-ups
• Custom WhatsApp-style chat history interface
• Flexible workflow automation using n8n
Tech stack:
• n8n
• Meta WhatsApp Business API
• Python
• REST APIs
• Phorest API
• Custom chat history system
This project strengthened my experience in AI automation, messaging platform integrations, workflow orchestration, and customer communication systems.
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40
I built an automation system that connects Telegram, n8n, Flask, and Playwright to simplify a complex browser-based workflow for field users.
The system allows users to interact with a Telegram bot instead of manually navigating a slow legacy portal. In the background, the bot manages browser automation, session handling, form submissions, and user-specific workflows automatically.
One of the main technical challenges was handling multiple browser sessions simultaneously without conflicts. Each user gets an isolated headless browser session with secure state management and automatic expiration handling.
The workflow also includes human-in-the-loop captcha solving. The bot sends the captcha image to the user, waits for the response, and then continues the automation process seamlessly.
Tech stack:
• Playwright for browser automation
• Flask for backend APIs and session management
• n8n for workflow orchestration
• SQLite for lightweight persistent storage
This project gave me hands-on experience building scalable automation systems that combine messaging platforms, backend services, and browser-level scripting for real-world operational workflows.
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