Akriti

Problem

Software projects begin with PRDs, architecture diagrams, ER diagrams, flowcharts, and UI mockups. Developers spend significant time understanding requirements, creating implementation plans, creating long prompts and then translating them into code.

Additionally, AI coding tools often require developers to repeatedly provide screenshots, code snippets, and lengthy prompts to explain the current state of their project.

Solution

Project-to-Code is a multi-agent AI software engineering assistant that understands project documents and diagrams, generates implementation plans, and orchestrates code generation using specialized AI agents.

Who Is It For? Software Developers Startup Teams Product Managers Technical Architects Hackathon Teams How It Works Understanding Agent

Analyzes documents, diagrams, architecture designs, and project artifacts to extract requirements, APIs, database entities, components, and user flows.

Planning Agent

Converts project understanding into development phases, tasks, dependencies, and execution plans.

Execution Agent

Uses project context and planned tasks to generate implementation-ready code and execution reports.

Browser & Workspace Context Extension

A lightweight extension continuously provides project context such as:

Current webpage or design Active project state Selected UI elements Open development context Workspace metadata

This eliminates the need to repeatedly upload screenshots or write long prompts explaining the current state of the project.

The AI receives live context and can make more accurate implementation decisions with minimal user input.

Why We Built It

Current AI coding tools are powerful at generating code but often lack complete project understanding and workspace awareness.

We built Project-to-Code to bridge the gap between requirements, planning, context awareness, and implementation by creating an end-to-end software engineering workflow powered by multiple AI agents.

Impact

Project-to-Code helps teams move from project ideas and documentation to implementation faster while reducing context-switching, repetitive prompting, and planning overhead.