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I recently worked on a project to build a real-time field data collector using ShahiRaj Data Collector, n8n, and Vector DBs. The goal was to empower field staff to log photos, locations, and site data in real-time, and make this data instantly available to management via a dashboard.

Here’s a high-level overview of the architecture: ShahiRaj Data Collector app collects data from the field, which is then forwarded to n8n via Webhooks. n8n processes the data and stores it in Vector DBs, which is then queried by our dashboard to provide real-time visibility.

Check out the code and architecture details for this project on our GitHub repository. #ShahiRaj #n8n #VectorDBs

I recently worked on building an AI Voicebot that acts as a high-speed assistant, answering calls instantly, qualifying the caller’s needs, and providing information 24/7. This project involved integrating Exotel with an AI-powered backend running on AWS Mumbai.

The solution uses a combination of natural language processing (NLP) and machine learning algorithms to understand the caller’s needs and respond accordingly. The backend is built using a microservices architecture, allowing for scalability and reliability.

One of the key challenges we faced was ensuring seamless integration between the AI-powered backend and the Exotel API. We achieved this by using Webhooks to trigger actions in real-time.

Now, let’s take a look at the tech stack used in this project:

• n8n as the workflow automation tool
• Vector DBs for storing caller information
• Webhooks for real-time communication between services

Check out the code repository and learn more about the technical details of this project.

Category: Developer Log

Tags: #ShahiRaj #AIVoicebot #TechnicalDeepDive #n8n

I’m excited to share the technical details behind our AI All-in-One Widget, the Multi-Channel Support Hub A8. This project was a thrilling challenge that required a deep understanding of AI, n8n, Vector DBs, and Webhooks.

At the core of the widget is a secure Debian 12 setup, which provides a robust foundation for our AI-powered support solution. We’ve integrated n8n to handle the workflow and Vector DBs to store the data. Webhooks enable seamless communication between platforms.

Explore the technical architecture behind the Multi-Channel Support Hub A8 and learn how we can help you build a similar solution for your business.

I’m excited to share my experience building the Instant Recall Secretary, an AI-powered visiting card scanner that uses n8n, Vector DBs, and Webhooks to revolutionize networking. The idea was simple – capture data, but capture momentum as well.

Here’s a high-level overview of the tech stack:

• n8n: The workflow automation platform that allowed me to connect various APIs and services.
• Vector DBs: The database that stores the scanned data, optimized for fast reads and writes.
• Webhooks: The real-time communication layer that enables seamless data transfer between services.

With this tech stack, I was able to create a solution that not only captures data but also empowers the busy professional to send follow-up messages in under 3 seconds. Check out Shahi Raj for more information on how we’re leveraging AI to simplify daily tasks.

Building the Instant Recall Secretary was a challenging but rewarding experience. I’m proud to have created a solution that can make a significant impact on people’s lives.

I recently led the development of ShahiRaj’s Custom RAG AI Agents, which have the potential to revolutionize how teams access company-wide knowledge. In this post, I’ll take you through the tech stack and architecture behind these AI assistants.

We’re leveraging n8n for workflow automation, Vector DBs for efficient data indexing, and Webhooks for seamless communication. The result is a 24/7 internal assistant that processes user queries in seconds, securely hosted on our AWS Mumbai infrastructure.

Want to learn more about the tech behind our Custom RAG AI Agents? Reach out to me and let’s discuss the possibilities.

I recently built a Zero-Typing Networking Assistant using n8n, Vector DBs, and AWS. The goal was to create a seamless experience for users to scan business cards and extract relevant information.

The workflow involves taking a photo of the card, sending it to our AI Bot, and then processing the data using n8n and Vector DBs. The extracted details are then saved directly into the user’s Excel or Google Sheet via our AWS Mumbai backend.

The result is a 3-second scanner that can accurately extract Name, Designation, Company, and Mobile No. without any manual data entry.

You can explore the entire workflow and code logic on my Developer Log.

As a developer, I’m excited to share the technical details behind our AI Lead-Response Assistant. Built using n8n and Vector DBs, this widget is designed to engage website visitors instantly and capture intent while your sales team is busy.

Here’s a high-level overview of the tech stack:

• n8n: Our workflow automation tool that triggers the lead response process
• Vector DBs: Our scalable and high-performance database for storing and processing lead data
• Webhooks: Our real-time integration mechanism for seamless communication between the widget and your sales team’s dashboard

When a website visitor interacts with our widget, the following process occurs:

1. The visitor’s intent is captured and stored in the Vector DB
2. A notification is triggered using n8n, sending an alert to the sales team’s dashboard
3. The sales team can then access the lead data in real-time, empowering them to follow up with precision

Our AI Lead Gen Widget is built with scalability and performance in mind, ensuring that it can handle high volumes of traffic and provide real-time insights to your sales team.

Want to learn more about the technical details behind our AI Lead-Response Assistant? Reach out to us and let’s discuss how we can tailor this solution to your specific needs.

#ShahiRaj #AIAssistant #TechnicalDeepDive

I recently built a personal assistant bot using n8n and Vector DBs that has revolutionized my Sunday prep. By integrating these tools, I can now organize my thoughts, summarize my week’s expenses, and prepare my Monday briefing with ease. The best part? I can do all this while disconnecting from work-related tasks and spending quality time with my family. If you’re interested in learning more about this project, check out our blog for a behind-the-scenes look.

I recently built a private AI assistant using Custom RAG AI Agents to help our team manage and access large amounts of data. The solution uses a combination of natural language processing (NLP) and machine learning algorithms to instantly search through thousands of private documents, SOPs, and project histories.

The solution is hosted securely on our AWS Mumbai backbone, ensuring the highest level of data security and compliance. I’m excited to share my experience and learnings from building this solution with the community.

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

• N8n workflow for data ingestion and processing
• Vector DBs for efficient data storage and retrieval
• Webhooks for real-time integration with other tools

Stay tuned for more updates on this project!

To learn more about Custom RAG AI Agents, check out our website or connect with us on LinkedIn. #ShahiRaj #AI #MachineLearning

I’m excited to share my recent project where I built a Custom Telegram Utility Bot that integrates with an n8n workflow and a PostgreSQL database.

The bot uses the n8n workflow to trigger actions based on user input, and the PostgreSQL database to store and retrieve live data.

To build this bot, I used the Telegram Bot API and the n8n workflow editor to create a seamless integration. The result is a ‘Command Center’ that empowers your team to make decisions instantly from the chat app they already use.

Ready to give your team a custom ‘Digital Assistant’ on Telegram? Learn more about our custom Telegram bot services and DM ‘COMMAND’ to discuss your workflow.

#ShahiRaj #CustomTelegramBots #n8n #PostgreSQL