The AI Data Cloud for Unstructured Data

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Document Ingestion

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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

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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

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Document Indexing and Transformation 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, leveraging GPU and TPU accelerators to eliminate the complexity of building distributed systems or managing hardware.

With auto-scaling capabilities, workflows scale down to zero when no data is being processed, ensuring you only pay for active data processing. When data is available, they scale up automatically to handle the workload.

Workflows can be automatically triggered when Document Ingestion API parses a document, thus letting you build end to end ingestion pipelines, where you delegate document parsing to Tensorlake but still build custom algorithms for chunking, summarization, and enrichment of parsed documents.

Use Cases:

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