Supported Formats

Talonic supports 25+ file types across four distinct processing paths. Each path is optimized for its file category — text files are read directly with zero latency, while complex document formats go through OCR to produce high-quality Markdown. The processing path is selected automatically based on the file extension.

The OCR path uses Mistral Document AI as the primary engine, with a Talonic API fallback if the primary service is unavailable. OCR converts documents to structured Markdown, preserving tables, headings, and layout information. For PDF files that exceed the configured chunk size (default 25 pages), the system automatically splits the document into page chunks, processes them in parallel, and merges the results — so even large documents are handled efficiently.

Image files follow the AI Vision path, where they are sent directly to the AI model for multimodal extraction. This means the AI "sees" the image and extracts data visually — useful for photos of receipts, scanned handwritten notes, or diagrams. If an image was previously OCR'd and produced meaningful Markdown (more than 100 characters), the system uses the Markdown extraction path instead, which enables richer quality metrics.

The text fast-path is the most efficient route: files like CSV, JSON, and plain text are read directly into memory with no external API call. This means they process almost instantly and incur no OCR cost. Email files (EML, MSG) are parsed to extract both the message body and any attachments, with each attachment processed as a separate document.

Email files receive special handling. EML files are parsed to extract both the message body and any attachments, with each attachment processed as a separate document linked back to the parent email. MSG files (Microsoft Outlook format) follow the OCR path and similarly extract embedded attachments. This means a single email file can produce multiple documents — the email body and each of its attachments — all processed independently through the full pipeline.

Format Detection in the API

When uploading files through the REST API, the platform detects the file format from the file extension and routes it to the appropriate processing path automatically. You do not need to specify the file type in the request: simply upload the file and the platform handles format detection, OCR engine selection, and extraction pipeline routing. The document detail response includes the detected mime_type and processing status, and the Processing Log tab in the web interface shows exactly which path each document took, stage by stage.

curl -X POST https://api.talonic.com/v1/sources/src_abc123/documents \
  -H "Authorization: Bearer $TALONIC_API_KEY" \
  -F "file=@financials.xlsx"

For bulk ingestion scenarios, consider the performance characteristics of each processing path. Text fast-path files (CSV, JSON, TXT) process almost instantly since they require no external API calls. OCR files (PDF, DOCX, XLSX) take longer because they pass through Document AI, but large PDFs are automatically chunked and processed in parallel to maintain throughput. Image files go through AI Vision which is the most computationally intensive path but produces excellent results for receipts, handwritten notes, and diagrams.

When choosing between file formats for the same content, prefer native digital formats over scanned images whenever possible. A native PDF with embedded text processes faster and more accurately than a scanned PDF image, because the native version can use the text fast-path or lighter OCR processing. Similarly, providing data in CSV or JSON format when available eliminates OCR cost entirely and produces the highest confidence extraction results. For spreadsheets, both XLSX and XLS formats are fully supported, along with the macro-enabled XLSM variant.

curl https://api.talonic.com/v1/documents/doc_7f3a1b2c \
  -H "Authorization: Bearer $TALONIC_API_KEY"

{
  "id": "doc_7f3a1b2c",
  "filename": "financials.xlsx",
  "mime_type": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
  "type_detected": "Financial Statement",
  "status": "completed",
  "extraction_count": 1
}