How Babelize Works

Babelize processes translations through a multi-stage pipeline designed for consistency and transparency. This page explains each stage and how they work together.

Translation Pipeline Overview

When you submit content for translation, it passes through the following stages:

Source Content → Preprocessing → Translation Engine → Post-processing → Output
                      ↓                   ↓
                 Glossary           Fallback Rules

Stage 1: Preprocessing

The preprocessing stage prepares your content for translation:

1. Format Detection: Identifies the file type (JSON, YAML, Markdown, etc.)
2. Structure Parsing: Extracts translatable text segments while preserving structure
3. Placeholder Protection: Identifies and protects variables, code snippets, and formatting markers

Stage 2: Translation Engine

The translation engine applies our deterministic AI model:

1. Glossary Lookup: Checks if any terms have predefined translations
2. Context Analysis: Examines surrounding text for accurate translation
3. Model Inference: Generates translations using our fine-tuned model
4. Consistency Check: Ensures identical phrases receive identical translations

Stage 3: Post-processing

After translation, the output is refined:

1. Grammar Verification: Checks for grammatical correctness in the target language
2. Format Restoration: Reinserts placeholders and restores document structure
3. Quality Scoring: Assigns confidence scores to translation segments

Deterministic Behavior

Babelize achieves determinism through several mechanisms:

Fixed Model Versions

Each project locks to a specific model version. Updates are opt-in, ensuring translations remain consistent until you choose to upgrade.

Seed-Based Generation

Our AI model uses fixed random seeds, eliminating variation between identical requests.

Configuration Hashing

Your glossary, language pair, and settings are hashed together. The same configuration hash always produces the same output.

What Happens When Translation Fails?

Babelize includes fallback mechanisms for edge cases:

See Fallback Mechanism for details.

Processing Time Factors

Translation time depends on:

• Content length: Measured in source characters
• File complexity: Nested structures take longer to parse
• Target languages: Some language pairs require more processing
• Current load: Queue depth affects wait times

Typical processing times:

Data Flow

Your content follows this path:

1. Upload: Content sent via dashboard or API
2. Queue: Request enters processing queue
3. Process: Translation executed in isolated environment
4. Store: Results saved with encryption at rest
5. Deliver: Webhook notification or polling retrieval

All processing occurs in memory. Source content is not retained after job completion unless you enable version history.