Competitor Intelligence Suite

This project started as a capstone, but evolved into something closer to a product.

The Competitor Intelligence Suite is a complete analytics tool that reads through corporate transcripts, pulls official financial data, monitors stock trends, and delivers both strategic insights and forward-looking forecasts. It’s designed for analysts and decision-makers who want to move from raw data to real answers — fast.

I built it to mirror what an in-house analytics product at a consulting firm or investment team might do, without needing multiple tools or licenses.

What it does

• Turns unstructured transcripts into boardroom-ready SWOT insights
• Connects narrative data to financial performance (via SEC and Yahoo Finance)
• Uses statistical forecasting (ARIMAX) to predict next-quarter returns
• Builds downloadable reports (PDF & CSV) for instant sharing
• All through a clean Streamlit interface that works offline

Why it matters

Companies say a lot in their earnings calls. Some of it is noise.
But some of it foreshadows movement — in performance, in stock price, in strategy.

This app doesn’t just summarise what was said. It:

• Measures sentiment over time
• Tracks mentions of key business units (buzz)
• Tags risks, opportunities, and strategic moves using RAG + LLMs
• Checks which variables (text, sentiment, financials) correlate with actual stock movement

Then it models the signal and forecasts forward.

In other words, it connects the story to the stock.

How it works (in plain English)

1. You upload a transcript (PDF or TXT)
2. The system breaks it into readable chunks
3. It asks smart questions like “What are the strengths?” and uses a local AI model to answer them
4. It finds supporting quotes, so you're never guessing where the insight came from
5. It saves everything in a polished PDF
6. Meanwhile, it also pulls the company’s financials (from the SEC), stock history, and text-based sentiment
7. It tests what variables might actually influence returns
8. Finally, it builds a predictive ARIMAX model and shows a forecast

Built with

• LLM + Retrieval: SentenceTransformers, FAISS, Ollama (LLaMA 3)
• Forecasting: statsmodels, ARIMAX, Granger causality
• Data ingestion: SEC API, Yahoo Finance, custom transcript parsers
• Interface: Streamlit
• Reporting: FPDF for downloadable PDFs

Example use case

Imagine uploading UNH_Q1_2024.pdf
→ You get:

• 3–4 focused SWOT bullets per category, backed by exact quotes
• A clean PDF you could walk into a client meeting with
• A sentiment/buzz trend showing concern or confidence over time
• Financials pulled straight from SEC filings
• A predictive chart showing the next 4 quarters of expected stock movement

It’s not just analysis. It’s explanation, direction, and justification — in one tool.

How to run it

Prerequisites

• Python 3.9+
• Ollama installed locally (to run LLaMA 3 offline)
• Internet for financial data APIs

Setup

git clone https://github.com/your-username/competitor-intelligence-suite.git
cd competitor-intelligence-suite
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt