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How I Built a 100% AI-Generated Document Translator with WCAG AAA & Zero-Data Retention

A developer built a full-stack document translator entirely through AI-driven 'Vibe Coding,' converting legal and bureaucratic texts into everyday Portuguese while enforcing WCAG AAA accessibility and LGPD compliance. The project, called Real-World Document Translator, uses a FastAPI backend with PII sanitization and Google Gemini API integration, and can run in a mock mode for offline testing.

read2 min views1 publishedAug 28, 2026

Overview:A full-stack, production-grade web application created entirely via AI/Vibe Coding. It converts complex legal terms, fine print, and bureaucratic bills into everyday Portuguese while enforcing WCAG AAA accessibility and strict LGPD compliance.

Recently, I set out to test the limits of Vibe Coding by building an end-to-end software product using AI-driven development.

The result is the Real-World Document Translator (Tradutor de Documentos da Vida Real). Its goal is to bridge the accessibility gap for elderly individuals, people with low vision, and folks with limited literacy—allowing anyone to understand legal and financial contracts without needing a lawyer.

Translating legal documents through AI presents two major technical hurdles:

Challenge Impact / Risk
Privacy & LGPD
Raw documents contain sensitive PII (CPFs, bank accounts, emails). Standard regex fails due to OCR noise (broken numbers, homoglyphs).
Accessibility
Most web tools ignore low-vision users and the elderly, lacking high contrast, scalable typography, and native screen reader tools.

Through iterative prompting and AI engineering, we designed a zero-trust, highly accessible architecture.

.env

variables.[ User ] ───▶️ [ Accessible Frontend (HTML/CSS/JS) ]

[ Secure BFF (FastAPI) ]

├── File Validation (Magic Bytes)

├── PII Sanitization (OCR-Resilient)

└── API Key Guard (.env)

[ Google Gemini API / Mock AI ]

Building this project 100% through AI was a major technical milestone. It proved that Vibe Coding isn't just about speeding up syntax—it's about directing AI to construct secure, accessible, and architecturally sound systems.

Key takeaways from this build:

Want to test it locally? Follow these simple steps using Python 3.10+:

bash
pip install -r backend/requirements.txt

python backend/server.py

Note: If no Gemini API key is configured, the server automatically defaults to Smart Mock AI mode for instant offline testing!
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