Build a Data Analytics App Using LangGraph | AI Agent for Data Analysis

Pavithra’s Podcast · Intermediate ·📊 Data Analytics & Business Intelligence ·4mo ago

Key Takeaways

The video demonstrates building a data analytics application using LangGraph, an AI agent for data analysis, showcasing its capabilities in providing intelligent data insights and comparative analysis.

Full Transcript

Hey everyone. Uh in today's video we're going to see about uh data analytics app that I built using Langraph. So back then we used to take data and uh build EDA uh manually. uh we don't have any tools and later we do have uh tools we did had uh tools and all but it was uh completely manual thing that we need to feed a lot of information to the tool or to the code and do this do that compare this with that and a lot of rules that we need to make but uh today we have AI and it is a few uh lines of code not even code we can just write a prompt for it and it can just do an entire analysis for us. So in this application you can compare your competitors you can analyze your own company or anything it may be uh use case is this and you can modify based on your uh uh idea. So here I just give a company and it understands how it is going and uh competitors uh what can be achieved and what all can be improved. So those insights it will give and this I orchestrated using langraph. So let's get started with so I'm just going to ask about uh promo. So once I done it search the web. So the ultimate goal is we need to do all together in uh 2 minutes within 2 minutes we need to get all the information in our hand. So that is the main goal of this application of like spending hours and hours for uh this entire uh uh inside building. We can just do this using few lines of prompts and finally we will get a a report. So we get uh uh an analysis a market sentiment analysis and 5-year growth trajectory and you'll have a relative competitive strength. I mean it compares with other uh uh its own competitors which is uh Antasporus, Aditas, New Balance, Puma I mean the same thing and uh under uh armor. So what you see is uh the best performer comparative with Puma. So Puma is with uh the entire history like brief uh insights on uh how it is doing what can be improved compared with uh its growth few market uh peers. So it is like giving like in thy insight just imagine if we are doing this you know with hand it's a like information which is available online and we are feeding one more thing is you can feed your own information to it and ask it to perform uh with that and this is uh quick on comparatively with anything that we are currently doing um I use a langraph for it in order to have this entire orchestration And uh yeah so I hope uh this thing a project idea which would be useful for u anybody who's like thriving into data analytics or into practice phase. So I can use uh this application for. So that's all I have. Thanks for watching. Bye.

Original Description

Want to build a Data Analytics app powered by AI Agents? In this video, I show you how to build a Data Analytics application using LangGraph step by step — from workflow design to intelligent data insights. You’ll learn: ✔️ How LangGraph powers structured AI workflows ✔️ Designing a data analytics agent ✔️ Connecting LLMs to structured data ✔️ Handling multi-step reasoning for analysis ✔️ Generating summaries & insights automatically ✔️ Real-world use cases for AI-driven analytics Perfect for data scientists, ML engineers, AI engineers, and anyone building agent-based analytics tools in 2026. By the end, you’ll understand how to turn LangGraph into a powerful AI data assistant. 🔗 Connect With Me & Resources: 💬 Discord Community: https://discord.gg/NymgnUrP 📸 Instagram: https://www.instagram.com/pavithravbhuvan/ 💼 LinkedIn: https://www.linkedin.com/in/pavithra-vijayan-6a68379a/ 🎯 Topmate: https://topmate.io/pavithra_vijayan
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This video teaches how to build a data analytics application using LangGraph, an AI agent for data analysis, and demonstrates its capabilities in providing intelligent data insights and comparative analysis. The application can be used to analyze market sentiment, compare competitor performance, and provide a 5-year growth trajectory. By using LangGraph, users can automate the data analysis process and gain valuable insights quickly.

Key Takeaways
  1. Design a data analytics workflow
  2. Use LangGraph to orchestrate the workflow
  3. Feed prompts to the AI agent
  4. Analyze market sentiment and competitor performance
  5. Compare results and provide insights
  6. Refine the application based on user needs
💡 LangGraph can be used to automate the data analysis process and provide intelligent data insights quickly, making it a valuable tool for businesses and organizations.

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