Sold by: INTERX
Deployed on AWS
DocumentAI is an AI-powered solution that converts unstructured documents including PDF, HWP, DOCX, PPTX into structured JSON data. It accurately parses text, tables, formulas, and code, enabling seamless integration into AI training, search systems, and automation workflows.
Overview
Document Parse AI Solution for Unstructured Document Parsing DocumentAI is an AI powered document parsing solution that transforms various unstructured documents into structured data. It supports a wide range of formats including PDF, HWP, PPTX, DOCX, and more. It precisely analyzes not only text but also tables, formulas, code, images, and other elements within documents. The parsed output is provided in a standard JSON format, arranged according to the natural reading flow, making it easy for developers to use in AI training, search systems, automation tasks, and more. Reduce preprocessing time and maximize the value of unstructured data with DocumentAI.
Highlights
- Key Strengths - Accurate Layout Analysis: Automatically detects document structure and provides high-quality structured data. Ensures consistent parsing across diverse formats and layouts. - Wide Format Support: Supports PDF, DOCX, HWP, PPTX, XLSX, and image files. Enables unified processing of documents from various sources. - JSON-Based Output: Delivers human-readable JSON results. Easily integrated into AI training, search, and automation systems.
- Key Features - Text OCR: Extracts text from images or PDFs, including metadata-based parsing. - Layout Analysis: Classifies content into 10 types (text, tables, formulas, code, etc.). - Table Parsing: Converts tables, including image-based ones, into structured HTML. - Formula Parsing: Extracts formulas from tables or standalone and converts to LaTeX. - Text Parsing: Segments and organizes text by semantic units. - JSON Output: Provides all results in a unified, readable JSON format.
- Key Applications - LLM Preprocessing: Structures large volumes of documents to build quality training datasets. - RAG System Development: Parses various formats to construct knowledge bases for RAG systems. - Enterprise KMS: Converts unstructured documents (e.g., reports, manuals) into searchable, reusable data. - Automated Report Generation: Generates formatted reports automatically using parsed data.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
g6.xlarge Recommended | $15.00 |
Dimensions summary
You pay for DocumentAI by the hour on a single g6.xlarge instance. This is usage-based pricing, so your cost tracks the number of hours you run the software. There are no tiers or plans to choose between. You scale spending simply by running the instance for more or fewer hours. The product parses documents by recognizing their structure and data types, then returns results in a merged format for downstream use.
Top-of-mind questions for buyers
You run DocumentAI on a single g6.xlarge instance, an AWS GPU-backed instance type. The hourly rate covers the software licence for that one instance. Underlying AWS infrastructure charges for the instance are billed separately by AWS. Each hour the instance runs counts toward your software cost.
Software charges accrue per hour the instance runs. A fully stopped instance does not accrue software charges. You control spending by starting and stopping the instance. Note that stopped instances may still incur AWS storage fees for attached volumes, billed separately from the software licence.
No. DocumentAI bills by instance-hours, not by the number of documents parsed or their size. Whether you process one document or many, cost depends only on how long the g6.xlarge instance runs. Heavier workloads may take more time, which raises hours indirectly.
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Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
This release introduces enhanced Document Parser features and infrastructure improvements for greater stability, scalability, and processing reliability.
- New APIs and Processing Capabilities
- Synchronous and asynchronous document parsing APIs
- Asynchronous processing status query with detailed progress and chunk-level information
- Asynchronous partial result retrieval for completed chunks
- Infrastructure Enhancements
- MongoDB for persistent storage of asynchronous parsing data
- Redis for high-performance asynchronous job processing and task coordination
Additional details
Usage instructions
Overview
This product provides a Document AI API that extracts structured information from documents (PDF, DOCX, PPTX, HWPX, HWP). The API supports synchronous and asynchronous processing and returns HTML, Markdown, text, layout coordinates, tables, formulas, and figures. After subscribing and launching the product, you can interact with the API using the endpoint assigned to your deployment.
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Important Notice After the first launch, please wait approximately 10 minutes before using the API. GPU-accelerated modules and background services require initialization time.
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Find Your API Endpoint Retrieve your API base URL from EC2 Public DNS, Load Balancer DNS, custom domains, or CloudFormation Outputs. Example: http://ec2-xx-xx-xx-xx.compute.amazonaws.com:1389
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Full API Documentation Full specifications available at: http://YOUR_ENDPOINT_HOST:1389/docs
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API Summary Sync Parsing: POST /api/anonymous/document_parser/document_ai/sync Async Job Submit: POST /api/anonymous/document_parser/document_ai/async Async Status Check: GET /api/anonymous/document_parser_history/async/status/{msg_id} Async Result Retrieval: GET /api/anonymous/document_parser_history/async/chunk_result/{msg_id}
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Synchronous Parsing curl -X POST "$API_ENDPOINT/api/anonymous/document_parser/document_ai/sync" -H "accept: application/json" -H "Content-Type: multipart/form-data" -F "file=@/path/to/file.pdf"
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Asynchronous Parsing (Submit Job) curl -X POST "$API_ENDPOINT/api/anonymous/document_parser/document_ai/async" -H "Content-Type: multipart/form-data" -F "file=@/path/to/file.pdf"
Response: { "status": 200, "code": "COMMON_SUCCESS", "data": { "msg_id": "uuid", "status": "start" } }
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Check Async Status curl -X GET "$API_ENDPOINT/api/anonymous/document_parser_history/async/status/{msg_id}"
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Retrieve Async Result curl -X GET "$API_ENDPOINT/api/anonymous/document_parser_history/async/chunk_result/{msg_id}"
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Response Overview { "msg_id": "string", "title": "string", "subtitle": "string", "filename": "string", "result": { "api": "string", "content": { "html": "string", "markdown": "string", "text": "string" }, "elements": [ { "category": "string", "content": { "text": "string", "markdown": "string", "html": "string" }, "coordinates": [ { "x": 0.0, "y": 0.0 }, { "x": 0.0, "y": 0.0 }, { "x": 0.0, "y": 0.0 }, { "x": 0.0, "y": 0.0 } ], "conf": 0.0, "id": 0, "page": 1, "order": 0, "relation": [0] } ], "model": "string", "pages": 0, "Latency_per_module": { "layout": 0.0, "formula": 0.0, "table": 0.0, "text": 0.0, "end2end_execution_time": 0.0 } } }
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Recommended Usage Use sync for small/medium files and async for large ones. Visit /docs for full details.
Support
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