Introduction

Scaffold is a specialized RAG (Retrieval-Augmented Generation) system designed to revolutionize how development teams interact with large codebases. Born from real-world frustrations with traditional documentation and AI-assisted development, Scaffold provides the structural foundation AI agents need to effectively construct, maintain, and repair complex software projects.

The Challenge

Modern development teams face three critical problems:

1. Documentation Decay
   Maintaining accurate and up-to-date technical documentation requires unsustainable manual effort

2. AI Context Blindness
   LLMs lack awareness of project-specific architecture and business logic, requiring inefficient manual context provisioning

3. Knowledge Fragmentation
   Critical system understanding exists only in tribal knowledge that's lost when team members leave

Our Solution

Scaffold transforms your source code into a living knowledge graph stored in a graph database. This creates an intelligent context layer that:

• Captures structural relationships between code entities
• Maintains both vector and graph representations of your codebase
• Enables precise context injection for LLMs and AI agents
• Supports construction, maintenance, and refactoring workflows

  Like its physical namesake, Scaffold provides the temporary support structure needed to build something great - then disappears when the work is done.

Project Organization

Schemas

Architecture Schema

Usecase Schema

Interfaces Schema

Internal Organization Schema

Project Structure

.
├── docs
│   ├── docmost   # Docmost Configuration
│   ├── img       # Static Images
│   └── research  # Research reports
└── src
    ├── core      # RAG Context Fetching Algorithms
    ├── database  # Graph/Vector Database Logic
    ├── generator # Abstract Tree Generator
    ├── mcp       # MCP Interface
    └── parsers   # AST Parcers

Team

Team Member	Telegram Alias	Email Address	Track	Responsibilities
Melnikov Sergei	@peplxx	s.melnikov@innopolis.university	Product Owner	Team Management, RAG Fetching Algorithms
Razmakhov Sergei	@onemoreslacker	s.razmakhov@innopolis.university	Developer	Languages parsers, AT Generation
Prosvirkin Dmitry	@dmitry5567	d.prosvirkin@innopolis.university	Developer	Vector, Graph Database Management
Mashenkov Timofei	@mashfeii	t.mashenkov@innopolis.university	Developer, Researcher	MCP, Signal interfaces
Glazov Sergei	@pushkin404	s.glazov@innopolis.university	QA, Researcher	QA, Market Research

FAQ

What is RAG (Retrieval-Augmented Generation)?

RAG (Retrieval-Augmented Generation) is a technique that enhances large language models (LLMs) by:

1. Retrieving relevant information from external knowledge sources
2. Augmenting the LLM's context with this retrieved information
3. Generating more accurate, context-aware responses

Unlike traditional LLMs that rely solely on their training data, RAG systems:

• Access up-to-date project-specific information
• Reduce hallucinations by grounding responses in actual codebase context
• Maintain knowledge beyond the LLM's token limit

How does Graph RAG work?

Graph RAG extends traditional RAG by representing knowledge as interconnected entities in a graph database.

References & Resources

Project Materials

• Excalidraw Board

• Google Document

• Weekly Report

• Weekly Role Distibution Table

Useful Themed Videos

• What is Retrieval-Augmented Generation (RAG)?

• GraphRAG vs. Traditional RAG: Higher Accuracy & Insight with LLM

• GraphRAG Explained: AI Retrieval with Knowledge Graphs & Cypher

• What is MCP? Integrate AI Agents with Databases & APIs

• Model Context Protocol (MCP) Explained in 20 Minutes