Conversational AI Chatbots for Customer Support 

I developed AI-powered chatbots for a healthcare provider and a home services company, automating customer interactions and reducing manual inquiries. These chatbots were designed to answer frequently asked questions, provide service details, collect lead information, and process appointment requests.

Initially, I built the chatbots using Voiceflow, which allowed for customizable conversation flows and integrations. However, I later transitioned to BotPenguin, as it provided built-in automation features that reduced the need for custom coding, making chatbot management more efficient.

Key Features Implemented:

• Instant Customer Support – The chatbots provided real-time responses to common inquiries, reducing wait times and minimizing repetitive questions for staff.

• Lead Capture & Data Collection – User inputs were structured into lead forms and automatically sent to a CRM system via webhooks.

• Appointment Request Processing – Users could submit booking requests, which were forwarded to the scheduling system for staff review.

• Multi-Platform Integration – The chatbots were embedded on websites and could be expanded to support social media messaging platforms.

By automating these processes, these chatbots significantly improved customer engagement, reduced manual support workload, and streamlined lead management for both businesses.

Appointment Booking & Inquiry Automation

To streamline appointment scheduling and inquiry processing, I developed an automated workflow using Make.com, eliminating the need for manual follow-ups. This system improved response times, reduced scheduling errors, and ensured a seamless experience for both users and staff.

The automation begins when a user submits an inquiry or booking request through a Google Form. The data is then stored in Google Sheets and processed using a Google Apps Script, which triggers a webhook to activate the workflow in Make.com.

Key Features Implemented:

• Automated Email Confirmations – Once a request is received, an email is sent to both the user and staff, confirming the submission details.

• CRM Integration – User data is automatically stored for future follow-ups and tracking.

• MailChimp Subscription (Opt-In Marketing Emails) – Users can choose to subscribe to marketing emails through a double opt-in process.

• SMS Notifications – A confirmation message is sent via Twilio, ensuring that users receive real-time updates.

• Automated Voice Call – After a short delay, the system triggers an auto-call using Twilio, playing a pre-recorded message to confirm appointment details.

• Calendar Scheduling – Booking requests are added to Google Calendar, with options for Zoom or Calendly integration as alternatives.

By automating these steps, this workflow reduced response times by 50%, minimized scheduling conflicts, and ensured a structured, hassle-free appointment booking process.

CRM Workflow & Lead Management Automation 

To improve customer tracking, lead follow-ups, and appointment management, I integrated automated workflows into an in-house CRM system. The system processed booking requests, lead data, and conversation history collected from chatbots and inquiry forms, ensuring that no lead was lost due to manual errors.

The CRM, called Dashcards, was developed internally by a senior developer. My role focused on operating and optimizing its data automation workflows, ensuring smooth integration between chatbots, booking systems, and lead management tools.

Key Features Implemented:

• Automated Lead Capture – Chatbot conversations and form submissions were sent directly to the CRM via webhooks, eliminating manual data entry.

• Conversation History Logging – Inquiry details and customer interactions were stored for better tracking and personalized follow-ups.

• Appointment Data Syncing – Booking details were automatically added to the CRM, ensuring efficient scheduling and reducing double bookings.

• Data Consistency & Workflow Optimization – Improved how lead information was categorized and structured, making retrieval and analysis easier for the team.

This automation significantly reduced manual data entry by 80%, improved lead response times, and enhanced customer follow-up efficiency.

AI-Generated Video Tutorials

As part of my Assistant Video Editor role, I worked on AI-powered content creation for AI Hustler, a YouTube channel focused on AI and automation tutorials. The goal was to automate video production by using AI tools to generate scripts, avatars, and voiceovers, reducing the time and effort required for content creation.

Key Features Implemented:

• AI-Powered Script Generation – Used AI tools to create structured, informative scripts for automation tutorials.

• AI Avatars & Voiceovers – Utilized HeyGen and other AI platforms to generate talking avatars and natural-sounding voice narrations.

• Video Assembly & Editing – Edited and structured videos using Canva or CapCut, ensuring professional presentation and smooth transitions.

• YouTube Content Publishing – Assisted in uploading and optimizing videos for audience engagement.

By leveraging AI for content creation, we streamlined video production, reduced manual editing time, and scaled tutorial output efficiently.

Social Media Automation & Research for Crypto Content Creation

I managed the social media automation and research operations for RazzTafari, a crypto influencer who created content on earning opportunities in blockchain and Web3 projects. My role involved both automating content distribution and conducting research to support his videos.

While my focus was on analyzing projects and preparing research reports, the final decision on which projects to feature was solely up to him. Many of these projects carried significant investment risks, and he ultimately decided to leave the crypto space due to its high-pressure and volatile nature. After his exit, I transitioned to my other work experiences.

Key Features Implemented:

• Automated Social Media Content Posting – Used Zapier to automatically post YouTube video updates to Facebook and Twitter, ensuring immediate content distribution.

• YouTube Video Announcements on Discord & Telegram – Configured Telegram and Discord bots to auto-post new video updates upon release, ensuring consistency across platforms.

• Crypto Project Research & Report Preparation – Conducted market research and risk assessments on various crypto projects, compiling findings into detailed research documents featured in his videos.

• Community Engagement & Automation – Managed auto-moderation tools on Telegram and Discord, reducing spam and maintaining community discussions.

Note on Social Media Links

The social media accounts I managed are now inactive following RazzTafari’s departure from the crypto space. As a result, visitors may see outdated content and unmoderated discussions. However, the automation systems I built remain a strong example of content distribution and engagement strategies in influencer-driven communities.

This automation reduced manual posting efforts by 90%, ensured consistent audience engagement, and helped streamline content research and publishing.

Automated Crypto Trading with Spot DCA Bots

To optimize trading efficiency and minimize manual market monitoring, I developed an automated crypto trading system using Spot DCA (Dollar-Cost Averaging) bots. This setup allowed for consistent trade execution based on predefined strategies, reducing emotional decision-making and improving risk management.

Key Features Implemented:

• Automated Trade Execution – Configured DCA bots to execute buy and sell orders at predefined price levels, ensuring gradual position accumulation.

• Risk Management & Capital Allocation – Applied automated stop-loss, take-profit, and trailing mechanisms to protect capital and optimize profits.

• Market Analysis & Strategy Implementation – Used historical price data and trend analysis to adjust trading parameters, ensuring efficiency in volatile market conditions.

• Hands-Off Portfolio Growth – Increased capital by 100%+ over two years through a data-driven, automated strategy that operated 24/7 without manual intervention.

This project highlights my ability to design, optimize, and manage process automation workflows, even in complex, high-risk environments like crypto trading.

DappSniper – Web3 Project Catalog & Risk Analysis

DappSniper was a Web3 project catalog developed for RazzTafari, aimed at listing and categorizing various crypto platforms, DeFi protocols, and blockchain-based earning opportunities. It served as a resource for evaluating different investment models, helping users discover and analyze new crypto projects.

The platform was developed by the same senior developer who built the CRM system I operated. While the dev handled the core development, I was responsible for managing DappSniper’s listings, testing platform features, and ensuring proper categorization of projects.

Key Features Implemented:

• Project Listing & Risk Categorization – Managed and organized crypto projects into structured listings, making it easier for users to evaluate them.

• Platform Testing & Debugging – Assisted in identifying issues, testing new features, and optimizing usability before updates were deployed.

• Feature Requests & UX Feedback – Provided input on site functionality, search tools, and data presentation to improve user experience.

• Lead Management & CRM Integration – Helped structure lead tracking workflows, ensuring project inquiries were logged efficiently.

DappSniper was eventually discontinued after RazzTafari exited the crypto space, but my experience managing the platform strengthened my data structuring, automation, and project evaluation skills within Web3 ecosystems.