X-doc AI

Translates documents across language pairs while maintaining semantic meaning, formatting, and domain-specific terminology through neural machine translation with context windowing. The system analyzes document structure (headings, lists, tables, metadata) and applies language-pair-specific translation models that preserve technical terms, brand names, and stylistic conventions rather than performing word-by-word substitution.

Maintains original document structure, layout, fonts, tables, and metadata during the translation process by parsing document AST, translating content nodes independently, and reconstructing the document with original formatting applied. This prevents common translation artifacts like broken table layouts, lost formatting, or corrupted metadata that occur when treating documents as plain text.

Processes multiple documents in parallel while maintaining terminology consistency across the batch through a shared translation memory or glossary that tracks term mappings across all documents. The system likely uses a two-pass approach: first pass builds a terminology index from source documents, second pass applies consistent translations across all files to ensure 'API endpoint' translates identically in document 1 and document 5.

Automatically selects and routes translation requests to specialized neural machine translation models optimized for specific language pairs (e.g., English-to-Japanese model vs English-to-Spanish model) based on source and target language detection. This allows the system to apply language-pair-specific training data, vocabulary, and linguistic rules rather than using a single universal model, improving accuracy for morphologically complex or distant language pairs.

Automatically identifies the language of input documents without requiring explicit language specification, using statistical language identification models that analyze character distributions, n-gram patterns, and linguistic features. The system likely returns confidence scores indicating certainty of detection, allowing downstream processes to flag ambiguous cases (e.g., documents with mixed languages or very short content) for manual review.

Evaluates translation quality using automated metrics (BLEU, METEOR, or proprietary scoring) and potentially human evaluation benchmarks, providing accuracy indicators for translated content. The system may compare translations against reference translations or use linguistic quality models to assess fluency, adequacy, and terminology correctness without human review.

Exposes translation functionality via REST API with asynchronous processing and webhook callbacks for long-running translation jobs. Clients submit documents via HTTP POST, receive a job ID, and are notified via webhook when translation completes, allowing integration into automated workflows without polling or blocking on translation latency.

Accepts documents in multiple formats (PDF, DOCX, TXT, etc.) and automatically detects format without explicit specification, routing to appropriate parsers and preserving format-specific metadata. The system uses file extension and content inspection to determine format, then applies format-specific parsing logic to extract text while preserving structure.