In this post, I’ll walk you through how I built a preschool readiness bundle using n8n, Vector DBs, and webhooks.
Step 1: Creating the Cut and Scissor mega-workbook in n8n.
Step 2: Integrating Vector DBs for structured learning and fine motor skills tracking.
Step 3: Setting up webhooks to connect with our Etsy shop and update product listings in real-time.
Want to see the full code and setup? Check out our GitHub repository: https://github.com/ShahiRaj/preschool-readiness-bundle
I recently built an n8n workflow to automate the process of posting digital mockups of our educational workbooks to social media platforms like Instagram Reels and Pinterest. The goal was to reach a wider audience and showcase our ‘Cut and Scissor Mega’ workbook. Here’s a brief overview of the workflow:
n8n workflow: (1) upload new book to cloud storage, (2) trigger n8n workflow, (3) generate digital mockup using Vector DBs, (4) post mockup to Instagram Reels and Pinterest using Webhooks
The end result is a seamless process that saves time and effort while ensuring consistent, high-quality content. Check out our latest workbooks and see how our automation workflow can benefit your business.
In this technical deep dive, I’ll showcase the development process of the Sales Scout, an AI-powered lead generation widget built using n8n, Vector DBs, and Webhooks.
The Sales Scout is designed to proactively assist website visitors by answering immediate questions and keeping potential leads engaged on your site longer. To achieve this, I leveraged n8n’s workflow automation capabilities to integrate with Vector DBs for data storage and Webhooks for real-time notifications.
The workflow involves the following steps:
By using n8n, Vector DBs, and Webhooks, I was able to create a scalable and efficient lead generation system that empowers sales teams to close high-value deals with confidence.
Want to learn more about building the Sales Scout? Get in touch with me to discuss the technical details.
#ShahiRaj #n8n #VectorDBs #Webhooks
I’m excited to share my experience building the AI Doctor Appointment Agent, a game-changing solution for healthcare organizations. This post dives into the tech stack and architecture behind the project.
We leveraged the power of n8n, a robust workflow automation tool, to connect with Vector Databases and Webhooks. This allowed us to create a seamless experience for patients and staff alike.
The AI Doctor Appointment Agent is built on AWS Mumbai, ensuring high availability and scalability. Our solution syncs in real-time with Google Calendar, providing patients with instant answers about doctor availability.
Check out Shahi Raj’s blog for more technical insights into our AI solutions.
Stay tuned for future posts where I’ll dive deeper into the code logic and architecture behind the AI Doctor Appointment Agent.
#ShahiRaj #AI #TechStack
I recently built an AI-powered Appointment Widget using n8n and AWS. The goal was to assist sales teams by showing their real-time availability and securing meetings instantly. Here’s a high-level overview of the tech stack and architecture:
n8n (Node.js) was used to handle the widget’s backend logic, integrating with AWS services for data storage and calendar syncing. The widget itself was built using React, with a focus on seamless user experience.
The real-time availability feature was achieved by using Vector DBs to store team availability data. Webhooks were used to trigger notifications and updates when a meeting is booked or cancelled.
Want to learn more about building a frictionless sales experience with n8n and AWS? Reach out to discuss further.
I recently had the opportunity to build an AI Support Chat Widget using n8n, Vector DBs, and Webhooks.
The widget’s primary function is to instantly answer FAQs from a knowledge base, allowing support teams to focus on high-priority issues.
To achieve this, I leveraged the power of n8n’s workflow automation, combined with the speed and scalability of Vector DBs, and the reliability of Webhooks for real-time updates.
The result is a seamless and efficient support experience for both support teams and customers.
Want to explore the tech stack behind our AI Support Chat Widget? Check out our technical documentation for a deeper dive.
I recently had the opportunity to work on an exciting project at ShahiRaj, building a scalable e-commerce tracking automation system using n8n and Vector DBs. The goal was to create a 24/7 proactive assistant for logistics teams, enabling them to focus on actual delivery issues rather than routine tracking calls.
To achieve this, I designed a system that integrates with Shipmozo/BigShip, sending automated updates to customers via WhatsApp as soon as their package status changes. This not only reduced the workload for the logistics team but also provided customers with instant updates, improving their overall experience.
The system is built on top of n8n, utilizing Webhooks to send and receive data. I leveraged Vector DBs for efficient data storage and retrieval, ensuring seamless communication between different components of the system.
Through this project, I gained hands-on experience with n8n, Vector DBs, and Webhooks. I’m excited to share my learnings and insights with the developer community, highlighting the potential of these technologies in building scalable and efficient automation systems.
Want to learn more about our e-commerce solutions and how we can help you automate your tracking process? Get in touch with us today!
Read more about our e-commerce solutions here. #ShahiRaj #EcommerceSolutions #Automation
We built our AI Lead Gen Widget using n8n as the backbone, enabling us to process and qualify leads efficiently. Check out our developer-focused technical guide to learn how we integrated Vector DBs and Webhooks to create a powerful lead qualification system.
As a developer, I’ve always been fascinated by the potential of AI to transform the healthcare industry. In this post, I’ll share my experience building a scalable AI Doctor Appointment Agent using n8n, Vector DBs, and Webhooks.
The AI agent acts as a WhatsApp bot, handling repetitive queries and freeing up staff to focus on patient care. Patients can book appointments 24/7, and the agent syncs instantly with the clinic’s calendar on our AWS Mumbai server.
The tech stack includes n8n for workflow automation, Vector DBs for scalable data storage, and Webhooks for real-time updates. By leveraging these technologies, we’ve created a highly efficient and effective AI Doctor Appointment Agent. Check out our GitHub repository to learn more about the code.
As a developer, I’ve worked on various projects where our team needed to access vast amounts of data quickly. That’s when I realized the potential of Custom RAG AI Agents. Shahi Raj built an AI assistant using PostgreSQL and AWS, which revolutionized our workflow. I’ll share my experience in building this Custom RAG AI Agent.
Here’s a high-level overview of the architecture:
The AI agent uses Vector DBs to enable fast vector similarity searches, making it possible to find the exact detail our team needed in seconds. With this setup, we reduced our team’s search time significantly, allowing them to focus on high-quality work. If you’re interested in building a Custom RAG AI Agent for your company, contact me or Shahi Raj to discuss further.