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:
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:
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.
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:
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.
In this post, I’ll dive into the technical details of building the ₹100 Professional Studio, a product photography solution that uses AI to generate professional scenes in under 3 minutes.
The studio uses a Telegram bot to receive raw photos from users, which are then processed using a combination of machine learning algorithms and computer vision techniques. The output is a set of 8 professional scenes, including marketplace backgrounds, lifestyle shots, and a transparent PNG.
But what makes this solution so powerful is the way it integrates with Google Drive to provide an SEO-ready folder for each product. This empowers users to scale their catalog without needing a photographer, making it a game-changer for e-commerce businesses.
Want to learn more about the tech behind the ₹100 Professional Studio? Check out our solutions and get in touch with our team to learn more about how we can help you grow.
#ShahiRaj #ProductStudioAI #AIpowered #EcommSolutions
In this post, I’ll walk you through building a 24/7 caller assistant using Exotel and AWS. The goal is to create a system that answers every call instantly, qualifies the caller, answers basic questions, and captures requirements with human-like precision.
We’ll use Exotel for handling incoming calls and AWS for storing and processing the caller information. I’ll share the code and architecture used to build this system.
Here’s the high-level architecture: Shahi Raj‘s AI Voicebot uses Exotel to receive incoming calls and then leverages AWS to process the caller information.
This allows us to create a 24/7 caller assistant that never sleeps and always has a professional front-line. Ready to build your own 24/7 caller assistant? Contact us for more information.
#ShahiRaj #Exotel #AWS #CallerAssistant
I’m excited to share my experience building ProductStudioAI, a cutting-edge solution that uses AI to generate high-quality product shots in under 3 minutes.
ProductStudioAI is built on top of n8n, a powerful workflow automation platform, and Vector DBs for efficient image storage and retrieval. Webhooks are used to integrate with Telegram, enabling seamless communication between the bot and users.
The AI engine is trained on a vast dataset of product images, allowing it to analyze and generate accurate and professional-looking shots. The entire process is automated, eliminating the need for manual intervention and ensuring consistency across all products.
One of the key challenges I faced during development was optimizing the AI engine for performance and scalability. By leveraging Vector DBs and n8n’s workflow automation capabilities, I was able to achieve impressive results and ensure a seamless user experience.
Ready to explore the technical details of ProductStudioAI? Head over to our website to learn more.
#ShahiRaj #ProductStudioAI #AIpoweredSolutions #TechnicalDeepDive