The AI Data Cloud for Unstructured Data

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Document AI

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Document Parsing

Parses any PDF, Word, or Presentation and performs post-processing steps like chunking. The Parsing API preserves the Reading Order and Layout of the document. It can extract information from charts, complex tables and hand-written notes.

The API can classify documents into one of the pre-defined categories or custom categories.

Use Cases: Indexing Documents into Vector Databases for building RAG Applications, Automated Document Routing, Summarization and Knowledge Graphs.

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Structured Extraction

Extracts structured data from documents based on a schema. Custom prompts can be passed into the API to further refine the extraction if needed.
The API handles unlimited context length, so you can feed it documents with 100s of 1000s of pages.

Use Cases: Business Process Automation, Data Entry into CRMs, Invoice Processing, etc.

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Serverless Data Workflows

Building AI Applications involves processing a lot of unstructured data, and often indexing them or performing structured extraction before feeding them into LLMs. Serverless Data Workflows enables building workflows in Python to process large volumes of data at high throughput or low latency

The workflows run on a fully managed infrastructure, including GPU and TPU accelerators, freeing you from hassles of building complex distributed systems, or managing infrastructure.

The workflows are exposed as highly concurrent REST APIs, enabling seamless integration with existing applications.

Functions scales down to zero when there is no data to process, you are only charged for the time the functions are actually processing data. They are automatically scaled up when there is data to process.

Use Cases: Building end-to-end RAG Pipelines, Audio Transcription, Data Labelling, Map-Reduce, Web Scraping and more.