MAATIQ AI: 10-Language Compliance Platform

How we built an AI-powered FDA compliance platform that serves manufacturers across Asia-Pacific in 10 languages — with multi-tier AI review and regulatory document retrieval.

Client Background

Manufacturers across Asia-Pacific needed a way to assess FDA compliance for food and cosmetics products without hiring expensive US consultants. The platform required multi-step AI review flows, accurate regulation retrieval, and support for 10 languages to serve a diverse international user base.

The Problem

FDA compliance is complex — different product categories have different regulatory pathways, each with multiple review stages. The AI system needed to handle this complexity accurately while being accessible to non-English-speaking manufacturers. The challenge was building a system where the AI's analysis was grounded in actual regulatory text, not general knowledge, across all supported languages.

What We Built

• Multi-tier AI review system — 4 layers for food, 5 for cosmetics, 4 for GRAS/NDI. Each layer progressively deepens the compliance analysis.
• pgvector RAG — vector similarity search retrieves the most relevant FDA regulations before the AI generates its review, grounding every analysis in actual regulatory text.
• 10-language support — 900+ translation keys covering the entire UI, AI review content, forms, and error messages via next-intl.
• Agent Workbench — a compliance agent dashboard for reviewing, accepting, modifying, or rejecting AI-generated assessments.
• OCR intake — document scanning for ingredient lists and product labels, feeding directly into the AI review pipeline.

Tech Stack

Building for 10 Languages

Multilingual SaaS adds complexity at every level:

• Layout flexibility — German text is 30-40% longer than English. Arabic reads right-to-left. Every component had to accommodate variable text length and direction.
• Translation management — 900+ keys across 10 languages. We used next-intl with structured JSON files for maintainability.
• SEO per language — each language variant needed proper hreflang tags, localized meta tags, and language-specific sitemaps.
• AI output localization — the AI review system generates responses in the user's selected language while referencing English-language FDA regulations.

Results

• 10-language support with 900+ translation keys.
• Multi-tier AI review: Food (4-layer), Cosmetics (5-layer), GRAS/NDI (4-layer).
• pgvector RAG for accurate regulation retrieval from the FDA knowledge base.
• Agent Workbench with full review/accept/modify/reject workflow.
• Deployed on Vercel + Cloudflare for global performance.

What Similar Businesses Can Learn

If you are building a SaaS that serves international users, plan for multilingual from day one — retrofitting translations into an existing codebase is significantly more expensive than building with i18n support from the start. For AI-powered applications, RAG (Retrieval-Augmented Generation) is essential for accuracy — LLMs without grounding in source documents will hallucinate regulatory information.

Frequently Asked Questions