Turn any investment firm's website into a clean, structured one-page profile automatically.

Investment teams often spend hours digging through firm websites, PDFs, and reports to extract key information like AUM, strategy, leadership, and team profiles. This project explores how AI agents can automate that workflow.

The system uses web navigation + LLM extraction to research a firm online and generate a polished GP tear sheet — similar to what analysts manually prepare during investment research.

Under the hood it combines:

• AI-guided web exploration
• structured data extraction
• automatic team headshot discovery
• dynamic HTML report generation

Instead of manually compiling firm summaries, the system builds them in seconds.

👉 Curious how it works?
Explore the project here: View Repository

An end-to-end AI pipeline for extracting structured information from complex documents.

Modern organizations deal with thousands of PDFs — contracts, licenses, reports, and financial documents — where the real value lies in the data hidden inside them.

This project builds a production-style document processing system that can:

• ingest documents
• perform advanced parsing and OCR
• extract structured fields
• normalize and store results for downstream applications

The architecture combines cloud AI services, GPU-based document parsing, and scalable processing pipelines to simulate a real enterprise deployment.

Highlights include:

• AI-driven document understanding
• GPU-accelerated parsing
• asynchronous processing workflows
• cloud-native storage and services

The goal isn't just document extraction — it's building a realistic AI infrastructure for document intelligence.

👉 See the full architecture and implementation:
View Repository

✨ More projects coming soon — exploring AI agents, web automation, and intelligent research systems.