Argus AI: Your Autonomous Market Analyst

Inspiration

We are overwhelmed by information. Between news, reports, and scattered data sources, extracting meaningful insights is time-consuming and inefficient.

I wanted to build something smarter: an AI that doesn’t just collect information, but understands it and explains what actually matters.

What it does

Argus is an AI-powered web intelligence platform that:

Automatically monitors and collects data from multiple online sources

Analyzes content using NLP (relevance, sentiment, entities)

Lets users ask natural language questions about their data

Uses Gemini to generate clear, contextual, and source-grounded insights

Instead of reading dozens of articles, users can simply ask: “What are the key risks this week?” and get a structured answer instantly.

How we built it

Backend: FastAPI with async workers for scalable crawling

Data pipeline: Scraping + processing + NLP (TF-IDF, cosine similarity, NER)

Database: MongoDB for document storage + Redis for task handling

Frontend: React + TypeScript dashboard

AI Layer (core): Google Gemini (RAG-based system)

Gemini is used to:

"Interpret retrieved data

Generate structured answers

Provide reasoning based only on trusted context"

Challenges we ran into

Async complexity: Debugging race conditions in background workers

RAG accuracy: Improving retrieval quality was harder than tuning the model

Latency: Handling response delays with large context inputs

Scope control: Cutting non-essential features to focus on a working MVP

Accomplishments that we're proud of

What we learned

In AI systems, data quality > model complexity

RAG performance depends more on retrieval than generation

Prompt design is critical for controlling AI behavior

Building with AI requires thinking in systems, not just features

What's next for Argus AI: Your Autonomous Market Analyst

Real-time alerting for emerging risks and trends

Multimodal ingestion (PDFs, charts, reports) using Gemini

Personalized insights based on user goals

Smarter summarization across time (trend evolution analysis)

Built With

• docker
• fastapi
• google-gemini-api
• mongodb
• python
• react
• redis
• scikit-learn
• spacy
• tailwindcss
• typescript