DeepTagger

Product Introduction

1. DeepTagger is a no-code AI platform designed to automate document processing by learning from user annotations to extract structured data from unstructured documents. It enables users to train custom AI models through a visual interface without requiring technical expertise or coding knowledge. The platform supports multiple file formats and uses deep-reasoning AI to handle complex document structures.
2. The core value of DeepTagger lies in transforming manual data extraction workflows into scalable, automated processes using a "Highlight-and-Label" training system. It eliminates dependency on rigid templates by allowing users to build fully custom models tailored to their specific document types. The platform reduces operational costs and accelerates data processing while maintaining high accuracy through its Subjective Reasoning Engine powered by large language models (LLMs).

Main Features

1. DeepTagger’s "Highlight-and-Label" interface enables users to train AI models by annotating key data points directly on documents, which the system uses to learn extraction patterns across similar files. This visual training method supports nested data structures, such as line items in invoices or clauses in legal contracts, ensuring precise capture of hierarchical information.
2. The platform operates without predefined templates, allowing users to build custom models for any document type, including financial reports, insurance claims, resumes, and logistics documents. It processes PDFs, Word files, images (JPG/PNG), text files, and other formats, with cloud-based storage for seamless scalability.
3. DeepTagger’s Subjective Reasoning Engine leverages LLMs to interpret context and intent, enabling advanced tasks like sentiment analysis, contextual categorization, and logic-based data validation. Results are delivered in real time, with options to refine predictions and export structured data via API or direct download in formats like CSV or JSON.

Problems Solved

1. DeepTagger addresses the inefficiency of manual data extraction from complex documents, which often leads to errors, high labor costs, and scalability challenges. Traditional template-based systems fail to adapt to document variability, but DeepTagger’s AI dynamically learns from annotations to handle diverse layouts and formats.
2. The platform targets non-technical teams in industries like finance, legal, HR, and logistics, where employees spend significant time processing documents but lack coding resources. It is particularly valuable for organizations dealing with high volumes of unstructured or semi-structured data.
3. Typical use cases include extracting financial metrics from annual reports, identifying clauses in legal agreements, parsing patient data from medical records, or capturing shipment details from logistics forms. It also automates resume screening by pulling skills, experience, and qualifications into structured databases.

Unique Advantages

1. Unlike competitors reliant on fixed templates, DeepTagger offers a fully flexible, template-free environment where models adapt to user annotations rather than predefined rules. This ensures compatibility with unique document formats and evolving business requirements.
2. The integration of a Subjective Reasoning Engine powered by LLMs enables context-aware extraction, such as distinguishing between similar terms based on document intent or inferring unstated data relationships. This goes beyond simple keyword matching to deliver actionable insights.
3. Competitive advantages include support for nested data extraction (e.g., multi-level tables), a no-code UI accessible to non-developers, and API integration for embedding AI capabilities into existing workflows. The platform’s free tier allows processing of 200 pages with full functionality, lowering adoption barriers.

Frequently Asked Questions (FAQ)

1. How does the "Highlight-and-Label" training process work? Users upload sample documents, highlight relevant text or data fields, and assign labels to train the AI model. The system analyzes patterns across annotated examples to automatically extract similar data from new documents, improving accuracy with iterative feedback.
2. What file formats does DeepTagger support? The platform processes PDFs, Word documents (DOC/DOCX), JPG/PNG images, text files, and other common formats. It automatically converts files to a standardized format for analysis, preserving original layouts during extraction.
3. Is coding required to use DeepTagger? No coding is needed—the entire workflow, from model training to data export, is managed through the visual interface. Advanced users can optionally use the API to integrate extracted data into external systems like CRMs or databases.
4. How does the free trial work? The free tier allows processing up to 200 pages without a credit card, offering full access to all features, including nested data extraction and API access. Users can upgrade to paid plans for higher volumes and enterprise support.
5. Can DeepTagger handle documents with tables or multi-level structures? Yes, the platform’s nested extraction capability identifies hierarchical data, such as invoice line items or resume sections, and organizes it into structured JSON or CSV outputs. Complex layouts are processed using deep-reasoning AI to maintain contextual accuracy.