Product features, releases, updates, roadmaps, and everything in between AI, automation, and data.
The article describes building an open-source document extraction system using Unstract, DeepSeek, Ollama for LLMs and embeddings, Unstructured.io for text/OCR, and PostgreSQL with PGVector for vector storage.
In this blog, we’ll explore the challenges engineers face when processing purchase orders. We’ll also highlight the purchase order OCR capabilities of LLMWhisperer and demonstrate how Unstract streamlines data extraction from purchase orders.
Combine Unstract’s document extraction with BigQuery’s storage and analytics to build an ETL pipeline, enabling efficient unstructured data processing for actionable insights and operational optimization.
Leverage Unstract’s extraction capabilities alongside Snowflake’s robust storage and analytics to create an ETL solution that maximizes the value of your unstructured data. This combination empowers businesses to make data-driven decisions and enhance operational efficiency.
Let’s compare how PyPDF and PyMuPDF handle PDF to text extraction, and see how LLMWhisperer offers improvements over these traditional libraries.
Learn how to extract line items from receipts using the OCR-powered scanning APIs from Unstract and LLMWhisperer in this comprehensive guide.
See Unstract in action with walkthroughs of core features and real extraction workflows.
Managed cloud, on-premise, or open-source. Unstract adapts to your infrastructure needs, so choose what works best for you.
Prompt engineering Interface for Document Extraction
Make LLM-extracted data accurate and reliable
Use MCP to integrate Unstract with your existing stack
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Make LLM-extracted data accurate and reliable
