Freemium
Leading digital analytics platform for product insights and customer journey analytics
analyticsproduct analyticscustomer insights
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Pricing
Freemium
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 29, 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?
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).
What is the difference between the Extract-Document service and the Guess-Shape service provided by the API?
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).
Which file formats and data types are supported for information extraction through the Waveline Extract API?
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).
