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I’m excited to share with you the technical details behind our Capital Guard solution.

Our AI-powered solution uses a combination of natural language processing (NLP) and machine learning algorithms to perform ‘Brand Moat Detection’ instantly.

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

• n8n workflow automation platform for data integration and processing
• Vector DB for fast and efficient data storage
• Webhooks for real-time notifications and updates

Our AWS Mumbai infrastructure provides the scalability and reliability needed to process large amounts of data in real-time.

Want to learn more about the technical details behind our Capital Guard solution? DM me and let’s discuss.

#ShahiRaj #CapitalGuard #TechnicalOverview #SmartSourcingAI_Bot

I built a Sunday Briefing workflow using n8n and Vector DBs to capture month-end reflections and organize ‘Must-Do’ lists. The workflow integrates with Debian 12 and AWS to assist with scheduling and expense tracking.

The architecture involves using n8n’s workflow automation features to fetch data from Vector DBs, which stores the user’s reflections and tasks. Webhooks are used to trigger the workflow on Sundays, ensuring a seamless ‘Sunday Reset’ experience.

Want to see the code? Check out the GitHub repo or contact us for more information on how to build your own Sunday Briefing workflow.

#n8n #VectorDBs #SundayBriefing #WorkflowAutomation #ShahiRaj

I built the SmartSourcingAI_Bot from the ground up, using a combination of n8n, Vector DBs, and Webhooks to perform ‘demand gating’ and identify low-velocity products. The system uses AWS Mumbai infrastructure to process data instantly and provide a ‘safe buy price’ for sourcing meetings. By integrating with Amazon’s API, the bot can stop analysis when velocity is too low, preventing unnecessary resource consumption. Check out the code and learn how to build a similar system for your clients. #ShahiRaj

As a developer, I’m excited to share the technical details behind the SmartSourcingAI_Bot, an AI-powered tool designed to assist Amazon sellers in making informed purchasing decisions.

The bot leverages n8n and Vector DBs to process and analyze data, providing real-time insights on ‘Brand Moats’ and ‘Safe Buy Prices.’ Webhooks are used to integrate with the AWS Mumbai infrastructure, ensuring seamless data processing and updates.

In this blog post, we’ll delve into the architecture and code logic behind the SmartSourcingAI_Bot, giving you a glimpse into the technical expertise that went into building this innovative solution. Visit our website for more information on the SmartSourcingAI_Bot.

#ShahiRaj #SmartSourcingAI_Bot #TechnicalDeepDive #n8n #VectorDBs

I’m excited to share my experience building a 24/7 AI Voicebot that acts as an ‘After-Hours Secretary’ for businesses. Using Exotel and AI integration, I created a solution that answers calls, provides instant info, and logs every requirement while the team is away.

The tech stack involved was n8n for workflow automation, Vector DBs for data storage, and Webhooks for real-time data exchange. I’ll be sharing the code logic and architecture behind this project in a future post.

Get in touch with me if you’d like to know more about this project or have any questions about the tech stack used.

I recently built the AI Lead Gen Widget using n8n, Vector DBs, and Webhooks, and I’m excited to share the technical details of this project. Shahi Raj is empowering businesses with 24/7 sales machines, and I’m honored to be a part of it.

The widget engages visitors instantly, answers questions, and qualifies their budget and needs. The flow is powered by n8n, which integrates with Vector DBs for efficient data storage and Webhooks for seamless communication with the sales team.

With the AI Lead Gen Widget, your sales team gets to Friday mornings with a list of pre-vetted leads in your AWS Mumbai-hosted database, ready for high-value conversations. It’s about turning traffic into a pipeline, 24/7.

Want to learn more about the technical aspects of the AI Lead Gen Widget? Reach Out to me, and let’s discuss how we can implement this solution for your business.

#ShahiRaj #AIProduct #LeadGeneration #SalesAcceleration #n8n #VectorDBs #Webhooks

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.