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I built the Misri Bot to provide a simple yet effective way for users to stay connected with the community. Using a combination of weather APIs and calendar services, the Misri Bot delivers accurate information to users in real-time. One of the key features of the Misri Bot is its ability to integrate with Official Mumineen.org timings, ensuring users always have the latest information at their fingertips.

From a technical standpoint, the Misri Bot is built using n8n and Vector DBs, with Webhooks used to deliver updates to users. If you’re interested in learning more about the technical side of the Misri Bot, check out our technical blog for more information.

As a developer, I’ve always been fascinated by the potential of AI in marketing automation. That’s why I built a system using n8n, Vector DBs, and Webhooks to automate manual tasks and increase lead generation.

The system uses Agentic AI to analyze data and make decisions in real-time, ensuring that our clients get the most out of their marketing efforts. By leveraging the power of AI, we can streamline our processes and focus on what matters most – growing our clients’ businesses.

Want to learn more about how I built this system? Check out the code on our GitHub page and see the power of Agentic AI for yourself.

I’m thrilled to share our team’s accomplishments in 2025, particularly in building a digital workforce using n8n. With over 400 production workflows live, we’ve tackled complex challenges and reached a staggering 800+ monthly e-commerce updates via Shipmozo/BigShip.

The key to our success? A robust infrastructure that can handle massive volumes. Last week, we made the strategic move to AWS, positioning us to handle 10x the volume we’re currently processing.

If you’re tired of manual work slowing you down, let’s make 2026 the year of ‘Set and Forget.’ I’ll be sharing more about our custom n8n workflow solutions here. #ShahiRaj #n8nworkflow

I’m excited to share my experience building @MisriCalendarBot, a Telegram bot that provides instant weather and prayer times for any city globally. As a developer, I wanted to create a bot that would be both efficient and accurate.

I chose to use AWS to power the bot, leveraging their speed and scalability to ensure that users receive the most up-to-date information. I also integrated Official Mumineen.org timings to guarantee the accuracy of our data.

One of the key features of the bot is its ability to handle user queries and provide personalized responses. I achieved this using a combination of natural language processing (NLP) and machine learning algorithms.

Overall, building @MisriCalendarBot was a rewarding experience that allowed me to push the boundaries of what is possible with Telegram bots.

In this post, I’ll walk you through how I built an automated lead funnel using n8n, Vector DBs, and Webhooks.

The goal was to create a system that could capture leads while the user is celebrating New Year’s Eve. To achieve this, I used n8n to connect to a Vector DB, which acts as a real-time database. When a new lead is generated, n8n triggers a Webhook that sends the data to a marketing automation tool. The result is an automated lead funnel that’s ready to go from day one.

Want to learn more about building an automated lead funnel with n8n and Vector DBs? Check out our blog for more technical insights and best practices.

I just spent the last 72 hours migrating our entire core to AWS, and it was a wild ride. But it was worth it – our new infrastructure is lightning-fast, super secure, and boasts 99.99% uptime.

As a developer, I know how crucial it is to have a solid tech foundation. But what does that mean for business owners? It means having a scalable, reliable, and efficient system that can handle the demands of the AI era.

We’re using n8n to create custom workflows and Vector DBs for blazing-fast data retrieval. And with Webhooks, we’re ensuring seamless integrations.

We’re opening 3 slots for Workflow Audits this week. Let’s identify and resolve your bottlenecks before Jan 1st. Reach out to schedule a call!

Over a weekend, we moved our production VM shahiraj (Debian/Docker) from GCP to its new AWS home. The goal was to take advantage of AWS Bedrock and AWS SageMaker for building even more complex RAG agents. We achieved this by parallel processing in the @MisriCalendarBot and our E-com Tracking bots, which now feel snappier on AWS.

Our cloud spend is now optimized, keeping our SaaS prices competitive for our clients. This migration has been a game-changer for ShahiRaj Automation, and we’re excited to see the impact it will have on our clients’ businesses.

If you’re interested in learning more about our migration story and how we’re using AWS to take our AI automation capabilities to the next level, check out the ShahiRaj Blog.

As a developer, I’ve had my fair share of frustration with traditional staffing methods. That’s why I decided to build a Commercial AI Agent using n8n, Vector DBs, and webhooks.

The goal was to create a system that could handle customer interactions with minimal human intervention. With n8n as the workflow automation engine, I integrated Vector DBs for efficient data storage and webhooks for seamless communication.

Now, our AI Agent can process and respond to customer queries in real-time, eliminating the need for extensive training or manual intervention. Shahi Raj can help you build a similar system for your organization.

Want to learn more about the tech stack behind our AI Agent? Reach out to schedule a consultation.

#ShahiRaj #AIDevelopment #N8n #VectorDBs #Webhooks

I recently built a scalable call center solution using n8n, Vector DBs, and our AI Voice Agents. The goal was to create a system that could handle an unlimited number of concurrent calls.

The solution involved using n8n to handle webhooks and API integrations, Vector DBs to store and manage call data, and our AI Voice Agents to answer and qualify calls in real-time.

One of the key challenges was ensuring that the system could scale horizontally. I used n8n’s workflow capabilities to distribute the load across multiple nodes, and Vector DBs to handle the high volume of call data.

The result was a system that could handle 50 concurrent calls without breaking a sweat. And the best part? It was all built using open-source tools and our AI Voice Agents. Learn more about our AI-powered solutions and discover how you can build a scalable call center that meets your business needs.

#ShahiRaj #CallCenter #n8n #VectorDBs

We’ve built a ‘Sales Copilot’ RAG agent to supercharge our sales team’s productivity. In this post, I’ll dive into the technical architecture and implementation details of our Sales Copilot.

Our Sales Copilot integrates with our Vector DBs, leveraging n8n workflows and Webhooks to provide instant, approved responses to technical questions. This enables our junior reps to ask the bot and get the perfect answer instantly.

I’ll walk you through the code logic and architecture behind our Sales Copilot, highlighting the key technologies and integrations that make it work.

Stay tuned for the next part of this series, where I’ll share more about the business impact of our Sales Copilot.

#n8n #VectorDB #SalesCopilot #ShahiRaj