The AI Data Cloud for Unstructured Data

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Document Ingestion API

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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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Serverless Data Endpoints

Tensorlake offers APIs to design and deploy custom data ingestion and transformation endpoints. These endpoints can be built via the Tensorlake SDK and exposed as REST APIs and this enables seamless integration with your applications or data pipelines.

These workflows operate on fully managed infrastructure, leveraging GPU and TPU accelerators to eliminate the complexity of building distributed systems or managing hardware.

With auto-scaling capabilities, functions 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.

Ingestion Endpoints can also integrate with Tensorlake Document Ingestion APIs for document parsing, transformation, and processing.

Use Cases:

• Building end-to-end ingestion pipelines for RAG
• Audio transcription
• Video analysis
• Data labeling
• Web scraping