Translation Pipeline
Zero-touch JP→EN document processing at scale using LLMs with cache-first batching and layout preservation.
The Problem
Manual translation is slow, expensive, and doesn't scale. When you're processing hundreds of documents across multiple formats (PDFs, presentations, spreadsheets), the bottleneck isn't just the translation—it's the entire workflow:
- Downloading files from shared drives
- Copying text from each document (sometimes page by page)
- Pasting into translation tools
- Reformatting because layouts break
- Reviewing and fixing translation errors
- Uploading translated versions back
This process could take weeks for a single batch of documents—and it's mind-numbing work that no one wants to do.
The Solution
I built a zero-touch translation pipeline that automates the entire workflow from Google Drive to translated output, preserving document formatting and leveraging LLM batch processing for efficiency.
Observability & Remote Operations
Production automation systems need visibility and control. Every component in this pipeline is designed for operations at scale:
- Structured logging: Every job event is logged with timestamps, job IDs, document metadata—enabling post-mortem analysis and pipeline optimization
- Health monitoring: Pipeline components report status via HTTP endpoints—know immediately if parsing fails, API rate limits are hit, or storage is inaccessible
- Remote control: Start, stop, and reconfigure jobs remotely without touching the server—adjust batch sizes, switch models, or pause processing from anywhere
- Failure recovery: Failed jobs are automatically queued for retry with exponential backoff—transient errors don't require manual intervention
- Progress tracking: Real-time status for each document shows current stage—queued, parsing, translating, formatting, or complete
This observability layer means the pipeline can run unattended for weeks. When something does need attention, you'll know exactly what and why—no silent failures, no mysterious missing documents.
Technical Approach
The pipeline is designed for throughput over perfection—it prioritizes processing speed while maintaining translation quality through human-in-the-loop review checkpoints.
- Async processing: Multiple documents processed concurrently using Python's asyncio
- Deduplication: Content-addressable caching avoids re-translating identical or similar text segments
- Format handlers: Specialized parsers for each document type (pdfplumber, python-docx, openpyxl, python-pptx)
- Error handling: Failed jobs are logged and can be retried with different parameters without reprocessing the entire batch
- Progress tracking: Real-time status updates for each document in the pipeline
The Result
What Changed
The pipeline processes hundreds of documents per hour—replacing what would previously have been weeks of manual translation work. The cache-first batching reduces API costs by ~60% compared to naive per-document processing, and layout preservation eliminates hours of post-translation reformatting.
Key metrics:
- ~100+ documents/hour processing capacity
- 60% cost reduction through intelligent caching
- Zero manual intervention for standard documents
- Human review required only for edge cases and QA
What This Means for Clients
Every business has document workflows that are slower and more expensive than they need to be. Whether it's translation, summarization, analysis, or data extraction—the same patterns apply:
- Manual work doesn't scale. If you're growing, you need automation.
- Format matters. Solutions need to work with real business documents, not just text.
- Cost control is possible. Intelligent caching and batching can reduce AI API costs by 50%+.
This pipeline is a template for document automation workflows. The same architecture can be adapted for contract review, financial document analysis, or any scenario where you need to process documents at scale.
Get in Touch
Need document automation for your business? I build systems like this. Get in touch to discuss your use case.