Welcome to Pulse API
Say hi to your new production-grade document extraction service, turning complex information into LLM-ready inputs. No training required. Our documentation provides an overview of our API endpoints, as well as templates on how to best utilize Pulse.
Extract clean training data for fine-tuning LLMs and building RAG systems
Process financial reports, contracts, and technical documentation at scale
We have two main processing endpoints, /extract and /convert. More information can be found in the API Reference.
Parse unstructured data into structured Markdown or JSON, specifying your own schema and chunking strategy.
For direct file uploads up to 6MB, get a presigned URL into our secure S3 storage system for processing.
Our API supports all documents, here are some examples:
Get up and running with our API in minutes
Detailed API specifications and examples
Here are some examples of AI-powered tools you can build with the Pulse API:
Process thousands of patient records, clinical trials, and medical journals to create a specialized healthcare knowledge base. Extract key medical findings, drug interactions, and treatment outcomes into vector embeddings for instant clinical decision support.
Transform your firm’s decades of case files, precedents, and legal opinions into a searchable knowledge base. Extract citations, legal arguments, and court decisions to help attorneys find relevant cases and build stronger arguments in minutes, not hours.
Streamline accounts payable workflows by automatically extracting and comparing data from purchase orders, receiving documents, and invoices. Our API processes these documents in real-time, identifying discrepancies in quantities, prices, and terms while maintaining an audit trail of all matches and exceptions for accounting compliance.
Build an AI agent that can autonomously analyze new documents, extract key information, and update its knowledge base. Perfect for staying current with regulatory changes, market research, or competitive analysis while maintaining context across millions of documents.
