AI-Powered Document Generation System

Project Overview

This project focuses on building an AI-driven document engineering platform that transforms unstructured or semi-structured business inputs into fully structured, production-ready technical deliverables.

The system automates the generation of:

Software Requirements Documents

Technical Proposals

High-Level Designs (HLD)

Low-Level Designs (LLD)

The core objective is to accelerate solution architecture workflows by reducing manual effort and ensuring consistency, quality, and scalability in technical documentation.

The platform follows a modular pipeline approach where each stage builds on the previous one, ensuring alignment between requirements, architecture, and implementation layers .

My Role & Contributions

I was responsible for designing and implementing the core intelligence and automation layer of the system, focusing on both requirements engineering and architecture generation workflows.

1. Requirements Engineering & Framework Design

Designed a requirement classification framework to handle multiple input types (from vague ideas to enterprise-level specifications).

Built logic to transform raw input into structured:

Functional Requirements (FR)

Non-Functional Requirements (NFR)

Constraints, assumptions, and risks

Ensured the system can handle messy or contradictory inputs and convert them into production-ready requirement documents

2. HLD & Architecture Planning

Defined a standardized High-Level Design (HLD) generation approach, including:

System decomposition

Service boundaries

Data flow design

Deployment architecture

Ensured alignment between business requirements and technical architecture

3. AI Orchestration & Automation (n8n)

Built a fully automated pipeline using n8n to orchestrate:

Input ingestion

LLM processing

Multi-stage document generation

Validation and approval flows

Designed modular workflows with:

Section-based generation

Merge and normalization stages

Feedback loops for iterative refinement

4. LLM Integration (Gemini)

Integrated Gemini (Vertex AI) as the core LLM engine

Designed structured prompts for:

Requirements generation

Architecture design

Technical proposal creation

Enforced strict JSON outputs for reliable downstream processing

5. Frontend & UX (Lovable + React)

Built the frontend using Lovable (React-based UI framework)

Focused on:

Clean user input experience

Structured output visualization

Interactive workflows for reviewing and refining documents

6. Cloud & Deployment

Designed a scalable architecture using:

Google Cloud Run for backend services

Cloud SQL for persistence

Ensured secure, scalable, and production-ready deployment

Tech Stack

Frontend: Lovable (React)

Backend: Node.js / TypeScript

Automation: n8n (workflow orchestration)

LLM: Gemini (Google Vertex AI)

Cloud: Google Cloud (Cloud Run, Cloud SQL)

Key Impact

Reduced manual effort in technical documentation by automating end-to-end document generation

Standardized requirements and architecture outputs across projects

Enabled rapid transformation from idea → architecture → implementation-ready design

Built a scalable foundation for AI-assisted solution engineering workflows