🏆 View Winners 🌟 View Sponsors 👥 All Participants

Manindra Reddy

Participant

SALESRAGER (AGENT )

Votes0

Score0

🧠Community0×5= 0

🌐Public0×1= 0

Individual

December 3, 2025 at 10:17 PM

Project Demo

Submission Details

hear-about-us: Linkedin

name: Manindra Reddy

College / University Name: Bapatla Engineering College

linkedIn-profile: https://www.linkedin.com/in/manireddy022

Project Title: SALESRAGER (AGENT )

Describe what your project does?: I’ve built an AI agent that combines traditional machine learning models with personal business data, integrating them through LangChain tool functions and using a Mistral LLM (via Ollama) as the reasoning engine. This architecture gives my project a unique edge. The ML component consists of two parts, each serving a distinct purpose: Sales Forecasting – A Facebook Prophet model predicts revenue for specific time windows such as 1 month, 4 months, or 6 months. Lead Conversion Prediction – An XGBoost classification model predicts the likelihood of a lead converting. A common question is how inputs flow through the system and how the final outputs are generated. Here’s how it works: When a user provides input—such as selecting a forecasting period (e.g., “next one month”)—the LangChain tool function structures this query into a validated JSON schema. This schema is then passed to the Mistral LLM, which interprets the request and triggers the correct backend ML model (Prophet or XGBoost). The sophistication of the system comes from how reliably LangChain maintains schema validation and ensures the LLM always sends accurate, structured inputs to the machine learning models. This creates a seamless interaction between natural language queries and precise model-driven outputs. If you'd like, I can also rewrite t

Share a 3–5 minute HD demo video: https://drive.google.com/file/d/19MsJXgnnVmU15ab_28P11J-sVBXKwJw5/view?usp=sharing

Hosted Project Link (if any)

GitHub Repository (if any): https://github.com/manireddy11/SALESFORECAST-AGENT-

artifacts/ supporting material: https://drive.google.com/file/d/1nbCauP2NSTYoL-gO1Zo8PbE0ypAs-Suy/view?usp=sharing

Is this project your original and proprietary work?: Yes

Does your project include any private, sensitive, or restricted code or content?: No

Do you give us (AI Demos & partners) permission to feature your work on our website and share it with our community (with full credit)?: Yes

Do you consent to us showcasing your name/profile on our social media platforms and YouTube: Yes

Are you open to freelance, internship, or collaborative opportunities based on this project?: Yes, I’m interested

Comments (0)

Explore More

All Participants Hackathon Details

Get In Touch

Connect with Manindra Reddy to learn more about their project.

LinkedIn Profile

Featured

FutureSmart Agent

A complete AI Agent system for real use cases. RAG, NL2SQL, API actions, multi-agent logic — everything in one place.

I’ve built an AI agent that combines traditional machine learning models with personal business data, integrating them through LangChain tool functions and using a Mistral LLM (via Ollama) as the reasoning engine. This architecture gives my project a unique edge. The ML component consists of two parts, each serving a distinct purpose: Sales Forecasting – A Facebook Prophet model predicts revenue for specific time windows such as 1 month, 4 months, or 6 months. Lead Conversion Prediction – An XGBoost classification model predicts the likelihood of a lead converting. A common question is how inputs flow through the system and how the final outputs are generated. Here’s how it works: When a user provides input—such as selecting a forecasting period (e.g., “next one month”)—the LangChain tool function structures this query into a validated JSON schema. This schema is then passed to the Mistral LLM, which interprets the request and triggers the correct backend ML model (Prophet or XGBoost). The sophistication of the system comes from how reliably LangChain maintains schema validation and ensures the LLM always sends accurate, structured inputs to the machine learning models. This creates a seamless interaction between natural language queries and precise model-driven outputs. If you'd like, I can also rewrite t