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As a developer, I’m always on the lookout for ways to improve productivity and streamline workflows.

That’s why I’m excited to share my experience building an AI Email Briefing & Summarizer using Groq AI, which helps scan unread emails across multiple Gmail accounts and delivers a clean, bulleted executive digest to Telegram.

Here’s a high-level overview of the architecture:

• n8n for workflow automation and email scanning
• Vector DBs for efficient data storage and retrieval
• Webhooks for real-time updates and notifications

By leveraging these technologies, we’ve created a powerful tool that helps business owners prioritize their morning workflow and make executive decisions with confidence.

Want to learn more about the tech stack behind our AI Email Briefing & Summarizer? Get in touch and let’s discuss further!

As a developer, I’ve always been fascinated by the challenge of extracting data from images. Our AI Visiting Card Scanner is a perfect example of this.

We use a combination of computer vision and machine learning to extract contact details from visiting cards. Here’s a high-level overview of our architecture:

• Image processing using Node.js and n8n
• Data extraction using Vector DBs and Webhooks
• Integration with Google Sheets and CRM systems

Want to learn more about our tech stack and architecture? DM me and let’s discuss!

Check out our blog for more tech insights

I recently built an AI Daily Briefing Bot for Shahi Raj, and I’m excited to share the technical details with you. Our bot uses n8n as the workflow automation tool, Vector DBs for storing and querying data, and Webhooks for seamless integration with Telegram. The bot consolidates calendar events, Misri dates, prayer times, weather, and holidays into a single, personalized message.

The bot is hosted on AWS Mumbai, ensuring high scalability and reliability. I used Node.js as the programming language and deployed the bot using Docker containers. The bot’s logic is designed to be modular, making it easy to add or remove features as needed.

Want to see how we built the AI Daily Briefing Bot? Get in touch with me to learn more about the technical stack and architecture.

#ShahiRaj #AI Daily Briefing Bot #n8n #Vector DBs #Webhooks

Building a custom AI brain for our AI Support Chat Widget is a complex task that requires a robust tech stack. We leveraged n8n for workflow automation, Vector DBs for indexing and querying, and Webhooks for real-time integration.

The result is a secure and scalable AI-powered support chat widget that can absorb up to 70% of routine support queries, freeing up your team to focus on complex tasks.

Check out our tech stack and learn how we built our custom AI brain.

As a developer, you know the value of a well-architected tech stack. Our AI Lead Gen Widget is built on top of the following technologies:

• n8n for workflow automation and data integration
• Vector DBs for efficient data storage and retrieval
• Webhooks for real-time event notifications and lead syncing

With this tech stack, our widget can engage visitors the second they land, qualify their intent, and capture contact details before they bounce. Check out our tech stack documentation to learn more about how we’ve designed our AI Lead Gen Widget for scalability and reliability.

I recently built an AI Voicebot using Twilio/Exotel to assist our team at Shahi Raj. Here’s a high-level overview of the architecture:

Architecture

We used n8n as our workflow automation tool and Vector DBs for storing caller information. Our AI Voicebot uses Webhooks to communicate with these systems.

Technical Details

• We used Node.js as our primary programming language.
• We utilized the Twilio API for making calls and handling voice interactions.
• We implemented a custom logic to qualify caller’s needs and provide basic FAQs.

Want to learn more about building your own AI Voicebot? Reach out to me at Shahi Raj and let’s discuss!

I recently built a custom mobile data engine for a client using n8n and Vector DBs. The goal was to create a seamless data collection experience for their field teams.

Using n8n, I set up a webhook that sent data from the mobile app to our server, which then synced with a Google Sheet in real-time. The Vector DBs provided a fast and efficient way to store and query the data.

The result was a clean, automated dashboard that provided valuable insights into their field operations. I was able to assist the data entry and empower the decision-maker by providing high-precision field intelligence.

Want to see how I can help you build a custom mobile data engine? DM me and let’s get started.

I recently worked on a project that aimed to automate the listing process for e-commerce sellers. The result was The Bulk Listing Express, a robust Auto Listing Engine that leverages AI, S3, and cron jobs to streamline the process.

The architecture of The Bulk Listing Express is built around the following components:

• AI-powered title and description generation
• S3 image hosting
• Hourly cron job submission

By integrating these components, we were able to create a high-velocity listing system that can handle large volumes of products with ease.

One of the key features of The Bulk Listing Express is its ability to process multiple products at once, reducing the manual effort required to list products on marketplaces like Amazon or Flipkart.

Want to learn more about the technical details of The Bulk Listing Express? Get in touch to discuss further.

In this post, I’ll dive into the tech stack that powers Shahi Raj’s Amazon Capital Protection system. We’re using n8n, Vector DBs, and Webhooks to identify ‘Brand Moats’ and provide ‘Safe Buy Price’ analysis. The system is designed to detect gated demand and help you make data-backed decisions.

Check out the code logic and architecture behind our Amazon Capital Protection system: https://www.shahiraj.com

I’m excited to share my experience building the Logistics Sentinel, an AI-powered e-commerce tracking solution that automates the monitoring of shipments. The system tracks over 800 Shipmozo and BigShip shipments hourly, leveraging the power of Vector DBs and Webhooks to provide real-time updates. By using n8n as our workflow automation tool, we were able to integrate various APIs and services, ensuring seamless data exchange and processing.

One of the key challenges we faced was developing a robust exception handling system, which would alert our users to any shipment delays or issues. Using Gmail and Telegram APIs, we created a notification system that would immediately alert our users, ensuring timely customer service and reducing the likelihood of ‘Where is my order?’ calls.

Want to learn more about the technical details behind Logistics Sentinel? Check out our GitHub repository for the code and architecture.