Waveline Extract

Freemium · Jan 30, 2026

A tool provides api to extract data from documents, images, and PDFs.

Waveline Extract is an AI-driven API service designed for developers to extract structured information from unstructured data sources. The platform utilizes large language models to process PDFs, images, and text, offering features like automated shape guessing and specific document extraction. It is built for technical users needing to automate data workflows for invoices, CVs, and emails. (verified: 2026-01-29)

Jan 30, 2026
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Pricing: Freemium
Last verified: Jan 30, 2026
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Key facts

Pricing

Freemium (as of Jan 30, 2026)

Use cases

Developers requiring structured JSON data from unstructured sources like plain text, emails, or PDF documents (verified: 2026-01-29), Businesses needing to extract specific fields from invoices and order tables using a predefined data shape (verified: 2026-01-29), Recruiters and HR professionals automating the extraction of candidate information from CVs and resumes via API (verified: 2026-01-29)

Strengths

The service utilizes large language models like GPT-4 and Claude to provide flexibility over traditional regex-parsing methods (verified: 2026-01-29), The Guess-Shape endpoint automatically suggests potential data fields to extract from a document to simplify schema creation (verified: 2026-01-29), The API handles common LLM issues such as hallucinations and incorrect formatting to ensure reliable JSON output for users (verified: 2026-01-29)

Limitations

Users must provide a specific Shape definition to describe the exact information they want to extract from documents (verified: 2026-01-29), The service requires developers to integrate via API endpoints as it is designed for programmatic data extraction (verified: 2026-01-29)

Last verified

Jan 30, 2026

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Strengths

  • The service utilizes large language models like GPT-4 and Claude to provide flexibility over traditional regex-parsing methods (verified: 2026-01-29)
  • The Guess-Shape endpoint automatically suggests potential data fields to extract from a document to simplify schema creation (verified: 2026-01-29)
  • The API handles common LLM issues such as hallucinations and incorrect formatting to ensure reliable JSON output for users (verified: 2026-01-29)

Limitations

  • Users must provide a specific Shape definition to describe the exact information they want to extract from documents (verified: 2026-01-29)
  • The service requires developers to integrate via API endpoints as it is designed for programmatic data extraction (verified: 2026-01-29)

FAQ

How does Waveline Extract handle the common formatting and hallucination issues associated with large language models? (recorded Jan 30, 2026)

As of Jan 30, 2026, our profile recorded: Waveline Extract manages the underlying processing using models like GPT-4 and Claude while implementing internal controls to mitigate hallucinations and formatting errors. This ensures that developers receive clean, structured JSON data without needing to manage the complexities of raw LLM outputs themselves (verified: 2026-01-29). Verify current details on the vendor site.

What is the difference between the Extract-Document service and the Guess-Shape service provided by the API? (recorded Jan 30, 2026)

As of Jan 30, 2026, our profile recorded: The Extract-Document service requires both a document and a specific shape to return structured JSON data. In contrast, the Guess-Shape service analyzes a document and automatically suggests the fields that are available for extraction, assisting users who do not have a predefined schema (verified: 2026-01-29). Verify current details on the vendor site.

Which file formats and data types are supported for information extraction through the Waveline Extract API? (recorded Jan 30, 2026)

As of Jan 30, 2026, our profile recorded: The platform supports a variety of unstructured data sources including plain text, PDF files, and images. It is capable of processing diverse document types such as invoices, emails, CVs, and order tables to convert them into structured formats (verified: 2026-01-29). Verify current details on the vendor site.