As a developer, I’ve always been fascinated by the potential of RAG AI Agents in automating complex tasks. In this post, I’ll take you through the technical process of building custom RAG AI Agents on Debian 12, leveraging our Debian 12 production environment.

We’ll cover the technical stack, including n8n, Vector DBs, and Webhooks, and explore how to integrate these components to create a robust and scalable AI solution.

Whether you’re interested in building custom AI agents or simply want to learn more about the technical aspects of AI development, this post is for you.

Stay tuned for the next part of this series, where we’ll dive deeper into the architecture and implementation details.

#ShahiRaj #CustomDev #RAGAIAgents #Debian12 #n8n #VectorDBs #Webhooks

We’ve built an AI-powered widget that unifies Lead Gen, Support, and Appointment Booking into a single, intelligent interface.

Here’s a technical overview of how we’ve implemented the widget:

N8n is used as the workflow automation tool, with Vector DBs as the primary storage for data.

Learn more about the tech stack behind our AI All-in-One Widget and how we’ve optimized it for high-speed performance.

I recently worked on a project where we built custom Telegram Bots that assist business owners by pulling live reports from their PostgreSQL database or Firestore directly into a private chat. Here’s a high-level overview of the tech stack we used:

n8n workflows to connect to PostgreSQL and FirestoreVector DBs for cachingWebhooks to trigger custom alertsA Debian 12/AWS setup to run the bot

The workflow involved creating a custom bot using n8n, which would pull data from the PostgreSQL database or Firestore using the Vector DBs for caching. We then used webhooks to trigger custom alerts and notifications.

Here’s an example code snippet that demonstrates how to connect to a PostgreSQL database using n8n:

const { n8n } = require('n8n');async function main() {  const database = new n8n.Database('postgresql://user:password@host:port/database');  const data = await database.execute('SELECT * FROM table');  return data;}main().catch((error) => {  console.error(error);});

If you’re interested in building custom Telegram Bots with PostgreSQL and Firestore, I’d be happy to help. Contact me for more information.

Tags: #CustomTelegramBots #PostgreSQL #Firestore #ShahiRaj

Category: Dev Log

I’m thrilled to share my experience building the AI Doctor Appointment Agent, a cutting-edge solution that’s revolutionizing the healthcare industry.

At the core of this agent lies a powerful n8n workflow, which integrates with Google Calendar and Vector DBs for seamless patient scheduling. The entire system is hosted on AWS Mumbai, ensuring scalability and reliability.

One of the key features of this agent is its ability to manage webhooks, allowing patients to book appointments via WhatsApp/Telegram in real-time. This has been a game-changer in reducing the stress of constant interruptions and enhancing the overall patient experience.

When building this agent, I focused on creating a scalable and maintainable architecture. The result is a solution that’s not only efficient but also easy to integrate with existing healthcare systems.

Want to learn more about the tech stack behind the AI Doctor Appointment Agent? Check out my developer log for a detailed overview.

As a developer, I’ve always been fascinated by the potential of n8n workflows to automate repetitive tasks. Recently, I built a custom n8n workflow that’s become the backbone of our silent operations engine.

Using n8n’s robust API and webhooks, I created a system that automatically routes e-commerce updates and syncs WhatsApp leads to our database. This not only reduces manual labor but also empowers our team to handle increased volumes without additional staff.

But what’s most impressive is the scalability of our custom workflows. Hosted on a Debian 12/AWS production server, our system can handle 10x the volume without any issues.

Want to learn more about building custom n8n workflows? Check out our developer resources and start building your own silent operations engine.

#ShahiRaj #n8n #CustomDev

I recently worked on a project that utilized the power of AI to revolutionize the way we handle visiting cards. Introducing the ‘Instant Lead-to-Sheet’ magic, powered by our AI Visiting Card Scanner.

Using n8n as the workflow automation tool, we integrated Vector DBs for efficient data parsing and Webhooks for seamless data transfer to Google Sheets or CRM via AWS Mumbai.

With this setup, we achieved under 3-second data parsing and transfer, making it an ideal solution for sales teams looking to streamline their workflow.

Check out the code repository and see how we built the Instant Lead-to-Sheet magic.

I’ve been experimenting with building an AI Personal Assistant Bot on Telegram/WhatsApp, and the results have been incredible. By leveraging Debian 12 as the underlying OS, I’ve been able to create a seamless experience for users.

The bot captures ‘random’ weekend ideas, organizes Monday priorities, and tracks spends in real-time, all while running silently in the background.

My goal was to empower users to be present with their families while the digital assistant takes care of administrative tasks. With this bot, I’ve achieved just that.

By focusing on automation and streamlining administrative tasks, I’ve been able to create a truly unique experience for users. Want to see how I built this? Check out the code or DM ‘ASSIST’ for more info.

I recently worked on a project to build a Custom RAG AI Agent that acts as a digital archive assistant for companies. The goal was to create a system that could search through a large amount of private company data, hosted securely on AWS Mumbai, and provide instant answers to team members.

The tech stack involved using n8n for workflow automation, Vector DBs for efficient data storage, and Webhooks for seamless integration. The AI agent was trained to read and understand the context of various documents, making it a valuable tool for research and decision-making.

One of the key challenges was ensuring the security and integrity of the data. We used AWS IAM roles to control access and ensure that only authorized team members could access the data.

With the ‘Digital Archive’ assistant, teams can now focus on high-priority tasks, knowing that their company data is always accessible and up-to-date.

Want to learn more about building your own Custom RAG AI Agent? Get in touch with our team.

As a developer, I’m always excited to talk about building solutions that make a real impact. Our AI Support Widget is one such solution that can transform your support team’s efficiency and performance.

By leveraging your own knowledge base and a secure Debian 12 setup, we can absorb 70% of routine queries instantly. This means that your human agents can focus on the complex, high-value customer issues that require their expertise.

So, how do we do it? We use a combination of n8n, Vector DBs, and Webhooks to create a seamless experience for both customers and support agents. Check out our tech stack

As a developer, I was tasked with building an AI Visiting Card Scanner that could read business cards and sync the data to a user’s Excel/CRM in under 3 seconds. Here’s a technical deep dive into the architecture and implementation of this project.

We used n8n as our workflow automation tool, Vector DB for our database, and Webhooks for communication between services.

The AI Visiting Card Scanner uses a combination of OCR (Optical Character Recognition) and machine learning algorithms to extract data from business cards. The extracted data is then sent to the user’s Excel/CRM via AWS Mumbai.

Want to learn more about the technical details of this project? Reach out to me and I’ll be happy to share more.