The AI Data Cloud for Unstructured Data

​

Document Ingestion

​

Document Parsing

Parses any PDF, Word, or Presentation and performs post-processing steps like chunking. It preserves the Reading Order and Layout of the document to enable an LLM to read documents as a human would. It can extract information from charts, complex tables and hand-written notes.

Use Cases:

• Creating Chunks from Documents for RAG and other retrieval applications
• Summarization and Knowledge Graphs

​

Structured Extraction

Extracts schema-guided structured data from documents. The API supports prompts for customization and processes vast amounts of data, handling documents with hundreds of thousands of pages.

Use Cases:

• Business Process Automation
• Data Entry into CRMs
• Invoice Processing

​

Workflows

You can build custom document ingestion and transformation workflows on Tensorlake. These workflows can be built using the Tensorlake SDK and exposed as REST APIs.

They run on fully managed infrastructure to eliminate the complexity of building distributed systems or managing hardware. They scale down to zero when no data is being processed, ensuring you only pay for active data processing.

Workflows can be triggered by the output of the Document Ingestion API, allowing you to build end-to-end pipelines for chunking, summarization, or enrichment of documents in the Tensorlake cloud without additional infrastructure.

Use Cases:

• Building end-to-end ingestion pipelines for RAG
• Summarization and Knowledge Graphs
• Data labeling