Argusa AI Challenge 2025 · Runner-Up 🥈

Secured 2nd place at the Argusa AI Challenge 2025 by building ARGRAG, a RAG system for complex enterprise document corpora with multi-modal analysis, metadata intelligence, and real-time performance.

Argusa AI Challenge 2025 · Runner-Up 🥈

Competed in the Argusa AI Challenge 2025 alongside Eisha Tir Raazia and secured 2nd place out of 20 teams with our project ARGRAG.

The Challenge

The hackathon focused on a common enterprise pain point: extracting reliable, explainable business insights from large volumes of scattered, unstructured internal documents. We had to:

  • Ingest heterogeneous document collections across many formats
  • Answer arbitrary business questions with high accuracy
  • Provide explainability via traceable sources and supporting evidence
  • Deliver interactive performance suitable for real users

Our Solution: ARGRAG

We built ARGRAG, a retrieval augmented generation system designed for complex enterprise document corpora.

Intelligent Document Processing

  • Support for 15+ file formats including PDFs (with OCR), Office documents, images, archives, and source code
  • Robust extraction and normalization across inconsistent inputs
  • Automated handling of archives (ZIP, TAR, 7Z, etc.)

Multi-Modal Analysis

  • Vision AI to interpret images, diagrams, charts, and embedded visual evidence
  • Automatic image description generation for context enrichment

Metadata Intelligence

  • LLM-generated corpus understanding to identify projects/teams, detect technology stacks, extract key concepts, and map document relationships

Multi-LLM Query and Answer Pipeline

  • Query augmentation using corpus metadata to expand user questions with relevant context
  • Vector search over ChromaDB for high-recall retrieval
  • Answer generation with explicit source attribution for explainability

Incremental Updates

  • Smart file watching with checksum-based caching
  • Efficient per-file incremental updates without full reindexing
  • Real-time change detection (1s polling)

Performance

  • Subsequent boot: < 5 seconds
  • Query response: 2 to 5 seconds
  • UI load time: < 1 second
  • File change detection: real-time (1s poll)
  • Incremental update: per-file basis

Tech Stack

  • Backend: FastAPI (Python 3.11+)
  • Vector DB: ChromaDB
  • Relational DB: PostgreSQL (document metadata and checksums)
  • Embeddings: SentenceTransformers (local, free)
  • Vision AI: Moondream and LLaVA (can run locally)
  • LLM: GPT-OSS (120B) via Ollama Cloud (large context, free within limits)
  • Frontend: React + Material UI

Team

Argusa AI Challenge 2025 · Runner-Up 🥈