Building a Gen-AI Pipeline to Localize and Reformat Educational Content
As an Agentic AI Intern at USDC Global, I was tasked with a fascinating challenge: how can we take dense, monolithic academic materials and transform them into something more engaging, accessible, and inclusive for a diverse student body? The answer lay in building a sophisticated, end-to-end automated pipeline that leverages the power of modern Large Language Models.
In this post, I’ll walk you through how I architected and developed a system that ingests raw PDFs, intelligently deconstructs them into “Byte-Sized Learning Modules,” and translates them into multiple languages, complete with rigorous quality checks. The initial vision for this project was conceived by my mentor and guide, Dr. Raja Subramanian (Head, Learning & Innovations Team), who empowered me to bring it to life.
The Problem: The Cognitive Overload of Course Material
The Learning Innovations team at USDC identified two primary obstacles in their existing Self-Learning Modules (SLMs):
- Information Overload: Students were presented with long, dense PDFs, often spanning over 50-100 pages. This format creates significant cognitive load and makes it difficult for learners to digest and retain key concepts.
- The Language Barrier: With content exclusively in English, students more comfortable with native Indian languages faced a significant barrier to effective learning.
The vision was clear: reformat these SLMs into Byte-Sized Learning Modules (BSLMs)—short, topically-focused chunks—and translate them to bridge the language gap. A manual approach was out of the question; it would be too slow, expensive, and impossible to scale. We needed an automated solution.
My Solution: A Modular, AI-Powered Pipeline
I designed and built an extensible framework to orchestrate the entire content transformation workflow. The system is fundamentally modular, allowing it to adapt to different content, languages, and AI models with minimal changes.
Core System Capabilities:
- Automated Content Ingestion: The pipeline seamlessly interfaces with enterprise APIs or local directories to pull in raw course materials.
- Intelligent Content Deconstruction: At the heart of the system is a proprietary AI-powered process that performs deep structural analysis of documents. It identifies logical sections and extracts them while perfectly preserving complex formatting like tables, LaTeX formulas, and lists.
- Dynamic Localization Engine: A configurable workflow manager orchestrates a multi-step translation sequence, capable of handling complex dependencies and even executing back-translations for automated quality assurance.
- Agnostic AI Provider Layer: The architecture features a powerful abstraction layer that makes all AI models—whether from Google, AWS, or another provider—interchangeable. This allows for strategic, on-the-fly optimization for cost, speed, and performance without altering the core logic.
- High-Fidelity Publishing: Final markdown outputs are rendered into professional, clean PDFs using Typst, a modern, code-based typesetting system that ensures a premium end-user experience.
Deconstructing the Workflow:
- Ingestion: The pipeline’s first stage identifies and acquires the target PDFs from their source, preparing them for processing.
- Orchestration: A central orchestrator takes command, managing the entire job flow for a batch of documents. It provisions resources and directs each PDF through the subsequent stages of the pipeline.
- Deconstruction: Here, the core intelligent extraction module gets to work. It analyzes the document’s semantic structure and returns a pristine, organized representation of its content, ready for the next stage.
- Localization: The localization engine takes the structured content and executes a pre-defined workflow of translations, ensuring each piece is handled correctly and in context.
- Publishing: In the final stage, the system compiles the translated content into organized markdown files and renders them as polished, distribution-ready PDFs using the Typst typesetting engine.
The power of this architecture lies in its strategic layers. The AI Provider Layer, for example, is an abstraction that allows the system to treat all AI models as interchangeable resources. This enables me to select the best tool for each specific task, constantly optimizing for the perfect balance of cost, speed, and quality.
Technology Stack
- Language: Python 3.10
- AI & LLMs: Google Gemini, AWS Bedrock Models
- Data Handling: Pandas
- Cloud & API:
boto3,google-genai,requests - Document Processing: Typst (for Markdown-to-PDF generation)
- Architectural Principles: Modularity, Abstraction, Scalability
Key Technical Challenges & My Solutions
1. Taming AI for Reliable Structured Data Off-the-shelf AI models can be unpredictable, especially when complex data formats are required. Getting them to return perfectly structured, machine-readable data every single time is a significant challenge.
- My Solution: I developed a sophisticated prompt engineering and validation framework. This wasn’t just about asking the model nicely; it involved creating a system of constraints, examples, and self-correction instructions that forced the AI to adhere to a strict output schema, increasing the reliability of valid, structured responses to near 100%.
2. Moving Beyond “Good Enough” Translation How do you prove, at scale, that a translation is not just grammatically correct, but semantically identical? Trust is paramount.
- My Solution: I designed a novel, automated validation process. After a primary translation, a second process would translate the content back to the source language. This result was then fed to a state-of-the-art model (Google’s Gemini 2.5 Pro) configured as a deterministic, analytical judge. This clinical comparison yielded a quantifiable similarity score, allowing us to consistently prove over 97% semantic accuracy.
Along with this, we also had human Subject Matter Experts (SMEs) curate the translated content from their native language, we had almost a 100% approval rate on this front as well. This means our framework was indeed working as expected.
Impact and Results
This project successfully transitioned from a conceptual idea to a production-ready pipeline, delivering significant and measurable value:
- Complete Automation & Unprecedented Speed: I automated a brand-new content workflow. The pipeline can process an entire course of 15 large PDFs—including chunking and translation into 3 languages—in under 30 minutes.
- Proven Scalability: The system has been tested on a corpus of over 300 source PDFs (~1 GB in total), demonstrating its stability and readiness for large-scale deployment.
- High-Fidelity Content Preservation: The intelligent chunking process successfully preserved complex academic content, including LaTeX mathematical formulas, data tables, and nested lists, during the PDF-to-Markdown conversion.
- Quantifiable & Verified Quality: Translation quality was validated at scale, achieving >97% semantic similarity in automated tests and receiving positive confirmation from manual reviews by native speakers.
- Demonstrated Framework Versatility: To test its limits, the framework was applied to a completely different domain: translating Hindi Jain religious texts into English for a new company initiative. Its success in this unrelated task highlighted the true language- and domain-agnostic nature of the architecture I designed.