Khushi jain@kj115

Modern software repositories contain a large amount of engineering knowledge, but that knowledge is fragmented across files, APIs, services, dependencies, architectural patterns, pull requests, technical decisions, and project conventions. Developers often spend significant time reconstructing this context before they can safely understand or modify an existing system. CodeContext AI addresses this problem by providing a unified developer context layer for the entire codebase. Instead of treating repository analysis, architecture understanding, code governance, pull-request review, technical decisions, and onboarding as separate activities, CodeContext AI connects them into one developer workflow. The platform begins with Repository Intelligence, which analyzes the repository structure and provides codebase-level context. Developers can use this information to understand important directories, technologies, services, and project organization without manually exploring the entire repository. Architecture Topology then converts the discovered structure into a connected system view. The application represents major layers such as the frontend, API layer, services, analyzers, and persistence, helping developers understand how components interact. The Guardrails module provides engineering governance by identifying violations, assigning severity levels, explaining the issue, and providing remediation guidance. This allows developers to identify potential problems before they become larger maintenance or production issues. PR Intelligence focuses on understanding proposed changes rather than simply displaying a code diff. It analyzes changes for risk factors, guardrail violations, testing concerns, reviewer questions, and suggested fixes. This gives reviewers structured information that can help them evaluate a change more effectively. CodeContext AI also includes Decision Memory, which preserves important technical decisions together with their rationale and consequences. Developer Onboarding uses the available repository context to create a guided path covering the repository overview, architecture, API layer, services, recommended files, and starter tasks. The application is built using React and Vite for the frontend, Python and FastAPI for the backend, SQLAlchemy and SQLite for persistence, and custom Python analysis modules for repository, guardrail, and pull-request intelligence. The result is a single developer workflow that moves from understanding the repository to understanding its architecture, governing the code, reviewing changes, preserving engineering knowledge, and helping new developers become productive. CodeContext AI was developed and validated using IBM Bob 2.0 as part of the engineering workflow, with Bob used for repository inspection, targeted improvements, implementation, testing, and verification.

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