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In this blog post, I’ll dive into the tech stack and code logic behind our Digital Sentinel, an AI-powered voice assistant that helps businesses manage customer queries on national holidays. The solution is built using n8n, a popular workflow automation tool, and Vector DBs for efficient data storage.

The AI assistant uses webhooks to receive and process incoming calls, leveraging natural language processing (NLP) to understand customer requests. The solution is hosted on AWS Mumbai infrastructure, ensuring high availability and scalability.

Our code logic involves a combination of Python and Node.js scripts, utilizing libraries like dialogflow and nlpjs for NLP tasks.

Stay tuned for more technical insights into our Digital Sentinel project.

#ShahiRaj #DigitalSentinel #TechStack

I’m excited to share our technical journey of building the MisriCalendarBot, a Telegram bot designed to provide instant access to essential information for the Dawoodi Bohra community.

The bot leverages the Mumineen.org API to fetch Hijri dates, Miqaats, and Namaz timings, which are then displayed directly on Telegram. We’ve also integrated the bot with our existing AWS workflows, ensuring seamless operation and scalability.

From a technical standpoint, the bot is built using Node.js and the n8n workflow automation tool. We’ve also utilized Vector DBs to store and retrieve data efficiently, and Webhooks to enable real-time updates.

Want to learn more about our technical approach? Check out our blog for more developer-focused content and stay tuned for future updates on the MisriCalendarBot.

#ShahiRaj #MisriCalendarBot #DeveloperLog

I built the Mental Bandwidth Assistant to demonstrate the power of automation and AI in personal productivity. The bot is built using n8n, a powerful workflow automation tool, and Vector DBs, a high-performance database.

The bot’s workflow is composed of several nodes, including:

• A Telegram webhook that receives user input and triggers the workflow
• A node that organizes weekend thoughts and ideas using Vector DBs
• A node that tracks personal expenses and finances using n8n’s built-in accounting features
• A node that pulls quick research and information using n8n’s web scraping capabilities

The bot is hosted on our AWS Mumbai server and uses a simple UI to interact with the user. The mental bandwidth assistant is a powerful tool for personal productivity and can be customized to fit your needs. Check out our blog for more information on building custom bots with n8n and Vector DBs.

As a seasoned developer, I’ve had the privilege of working on some amazing projects, but building custom n8n workflows for businesses has been a game-changer. With n8n, I can create complex automation flows that connect disparate systems and processes.

One of my favorite projects involved building a workflow that integrated with a CRM, sent notifications to a sales team, and scheduled follow-ups. The result was a seamless and efficient process that freed up the team to focus on high-value tasks.

When building custom n8n workflows, I always keep in mind the importance of reliability and scalability. That’s why I host my workflows on AWS Mumbai infrastructure, ensuring that data moves accurately and instantly.

Want to learn more about building custom n8n workflows? DM me, and let’s get started!

Check out my experience with n8n workflows and how they can transform your businessShahi Raj.

Let’s dive into the tech stack used to build our AI Voicebot, which assists callers after hours and syncs appointments to the calendar via Exotel and AWS.

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

• n8n: A node-based workflow automation tool
• Vector DBs: A real-time data storage solution
• Webhooks: For seamless integration with Exotel and AWS

We’ve implemented a custom solution using a combination of these technologies to enable our AI Voicebot to:

• Answer FAQs and take detailed notes
• Sync appointments to the calendar

Check out the code repository for our AI Voicebot

I recently worked on a project that involved building an E-commerce Support Shield using n8n and Vector DBs. The goal was to create an AI-powered tracking automation system that would send real-time updates to customers via WhatsApp. Here’s a high-level overview of the architecture:

n8n was used as the workflow automation tool to integrate with the customer’s e-commerce platform, parcel tracking API, and WhatsApp messaging service. Vector DBs provided a scalable and high-performance database for storing and querying order tracking data.

By implementing this solution on AWS, we were able to automate repetitive tracking updates and empower the customer’s human team to focus on high-touch inquiries. If you’re interested in learning more about this project and how we used n8n and Vector DBs to achieve this, feel free to reach out.

I recently built an AI Personal Assistant using n8n, Vector DBs, and Webhooks, and I’m excited to share the technical details with you.

The AI Personal Assistant is designed to act as a digital ‘Right Hand’ on Telegram, assisting users in making informed decisions by pulling data from Postgres/AWS servers or the web in seconds. To achieve this, I used the following technologies:

• n8n: A visual workflow automation tool for building and executing workflows.
• Vector DBs: A high-performance, distributed database for storing and retrieving data.
• Webhooks: A lightweight, real-time communication protocol for integrating with external services.

By combining these technologies, I was able to create a seamless and efficient workflow that empowers users to focus on their goals rather than getting bogged down in administrative tasks.

Want to learn more about building your own digital sidekick? Check out our blog and DM ‘ASSIST’ to see how our AI Personal Assistant manages our day.

As a developer, you know the struggle of searching for the right information. That’s why I built our custom RAG AI agents using n8n, Vector DBs, and Webhooks. Read more about the technical stack and architecture behind our expert knowledge assistants.

Want to learn more about the tech behind our RAG AI agents? Check out our developer resources and get started with building your own.

I recently worked on a project to build an AI All-in-One Widget for Shahi Raj. The goal was to create a widget that could assist visitors instantly and empower the sales team with pre-qualified leads. In this post, I’ll take you through the technical aspects of building this widget.

The widget is powered by a combination of n8n, Vector DBs, and Webhooks. We used n8n to integrate with various APIs, Vector DBs to store and manage leads, and Webhooks to trigger automated workflows. The backend is built on AWS Mumbai, ensuring a fast and reliable experience for our users.

In terms of code logic, we used a combination of JavaScript and TypeScript to develop the widget. We also leveraged various libraries and frameworks to simplify the development process.

Want to learn more about the technical aspects of building the AI All-in-One Widget? Check out our documentation for more details.

In this post, I’ll walk you through how to build a Custom Telegram Utility Bot using n8n and PostgreSQL. This bot will act as a central hub for your operations, enabling your team to pull data and trigger workflows with a simple command.

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

• n8n as the workflow engine
• PostgreSQL as the data store
• Telegram as the communication channel

In this example, we’ll use the n8n Telegram webhook to receive updates from the bot and trigger workflows accordingly. We’ll also use the PostgreSQL driver in n8n to pull data from the database.

Here’s some sample code to get you started:

const n8n = require('n8n');const pg = require('pg');const telegram = require('node-telegram-bot-api');const bot = new telegram('YOUR_TOKEN');

Want to learn more about building Custom Telegram Utility Bots? Check out our resources page for more information.