Frequently Asked Questions About DeepTagger
What types of documents and file formats can DeepTagger process?
DeepTagger natively supports PDF (including scanned images), Microsoft Word (.docx), JPEG, PNG, and plain-text (.txt) files. It handles multi-page documents, mixed-format batches, and even documents with embedded tables, watermarks, or rotated text—no preprocessing required. Specialized handling for financial statements, insurance policies, and government forms is built into its reasoning layer.
Is DeepTagger a no-code platform, and how easy is it to use?
Absolutely. DeepTagger was designed from the ground up for non-technical users. There’s no coding, no regex, no template libraries to browse. If you can highlight text in a browser and type a label, you can train and deploy a custom AI extraction model. Onboarding takes under 5 minutes—and most users achieve >90% accuracy on their first document after just 3–5 labeled examples.
How does DeepTagger ensure high accuracy in data extraction?
Accuracy stems from three pillars: (1) A deep-reasoning LLM backbone trained specifically on document understanding—not generic text; (2) K-Score Analytics, which surfaces low-confidence extractions for review before export; and (3) One-out Testing, which automatically tests model robustness against document variations and suggests retraining opportunities. This closed-loop validation drives continuous improvement.
What is DeepTagger's pricing model?
DeepTagger offers a fully functional free tier: process up to 200 pages per month, no credit card, no time limit. For teams needing higher volume, advanced analytics, SSO, or dedicated support, transparent monthly or annual plans are available. Custom enterprise agreements—including private cloud deployment and SLA guarantees—are offered upon request. Book a free 30-minute guided demo to explore your specific use case.
Can DeepTagger handle complex document structures?
Yes—complexity is DeepTagger’s strength. Its nested extraction engine parses hierarchical relationships inherently: think purchase orders with header-level PO numbers *and* item-level SKUs, quantities, and delivery dates—all preserved in nested JSON arrays. It handles repeating sections, conditional fields, multi-column layouts, and documents where the same label appears in multiple contexts (e.g., “Address” for both Ship-To and Bill-To)—distinguishing them intelligently using spatial and semantic cues.
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