DeepTagger

DeepTagger is a cloud-based document processing platform that utilizes advanced AI technology to easily train AI through simple highlighting and annotation interfaces, thereby automatically extracting required data from various documents. The importance of this product is that it greatly simplifies the document data processing process and improves work efficiency. Its main advantages include no code and complex templates, support for multiple file formats, the ability to build custom models, the ability to perform nested data extraction and in-depth reasoning. Product background: Committed to solving the problems of enterprises in document data processing. In terms of price, users can process up to 200 pages of documents for free without credit card information. The product is positioned to provide efficient and convenient automated document data processing solutions for all types of enterprises.

Demand population:

["Enterprise financial personnel: Corporate financial personnel need to process a large number of financial reports and financial documents on a daily basis. DeepTagger can help them quickly extract key data from these documents, such as income, expenditure, profit, etc., greatly improving the efficiency of financial analysis and statement production. ", "Legal practitioners: Legal practitioners will be exposed to various legal contracts and legal documents. Using DeepTagger can quickly extract important information such as terms, obligations, periods, etc. in the contract, which will help them better conduct contract review and legal research.", "Human Resources Experts: Human Resources Experts will receive a large number of resumes during the recruitment process. Through DeepTagger , key information in the resume, such as work experience, educational background, skills, etc., so as to screen candidates more efficiently. ", "Logistics Industry Practitioners: The logistics industry involves a large number of logistics documents, such as waybills, warehousing records, etc., DeepTagger can quickly and accurately extract logistics information, such as cargo information, transportation routes, delivery time, etc., which helps optimize logistics processes and improve operational efficiency. "]

Example of usage scenarios:

Financial institutions: Use DeepTagger to extract key financial indicators, such as revenue, profit, assets and liabilities, etc. from quarterly financial reports, for financial analysis and decision-making.

Legal Firm: Use this platform to quickly extract information such as terms, obligations and responsibilities from legal contracts to assist lawyers in contract review and risk assessment.

Recruitment company: Use DeepTagger to automatically extract candidates' work experience, skills, educational background and other information from a large number of resumes to improve recruitment efficiency.

Product Features:

Users can use the highlighting and annotation interface provided by the platform to intuitively train powerful AI, allowing them to accurately extract the data needed by users without writing code or using complex templates.

The platform supports uploading any file type, such as PDF, Word documents, JPG images, PNG images, text files, etc. Cloud-based solutions can receive all documents to build automated workflows.

After completing data training, the platform can quickly give prediction results, users can quickly optimize the results and export the extracted data within a few seconds for easier subsequent use.

The platform can immediately extract data from various documents such as financial reports, legal contracts, insurance claims, logistics documents, resumes, etc. to meet the information acquisition needs in different scenarios.

It is a completely template-free platform where users can build truly customized AI models for any document, get rid of the dependence on suppliers to provide templates, and fully meet personalized needs.

With the Core Large Language Model (LLM), the platform’s subjective reasoning engine can go beyond simple data extraction, understand the context of the document and explain its intent, thereby mining deeper insights.

The platform supports nested structures that can accurately process complex documents, such as invoices with multiple line items, ensuring the accuracy and completeness of data extraction.

Tutorials for use:

Step 1: Upload the document. Users can use the platform to upload any type of files, and their cloud-based solutions are able to receive various documents, preparing for subsequent construction of automated workflows.

Step 2: Highlight and mark. Use the intuitive highlighting and labeling interface provided by the platform to mark the required data to train the AI ​​to understand the user's data extraction needs. In the process, there is no need to write code or use complex templates.

Step 3: Prediction and Export. After completing data training, the platform will immediately give the prediction results. Users can quickly optimize the results and export the extracted data within a few seconds for subsequent processing and use.