Serverless Data Applications
Build your first Agentic Data Application with production level reliability and scale in minutes.
Document Ingestion API
Convert documents and images into Markdown, extract structured, data or classified pages.
Key Features
Building data applications is cumbersome. They require working with queues, storage systems, workflow engines, UDFs in SQL, and infrastructure tools like Kubernetes and Terraform. Tensorlake provides the fastest way to build and scale data applications:- Zero operations Your application runs automatically when it receives an HTTP request. No Kubernetes or Terraform stacks to manage. You only pay for the compute tied to actual business impact.
- Queue free architecture There is no need to wire up queues to handle large volume of requests, retries, and workflow orchestrators. Tensorlake scales your applications as more requests come in.
- Familiar programming model Build in Python, use any libraries you want. You don’t have to use Spark or bolt UDFs into SQL to process data. Tensorlake gives you distributed, crash-proof execution without a learning curve.
- GPU and CPU processing and autoscaling Tensorlake supports both GPU and CPU processing, and automatically scales your applications based on demand.
- Built-in Document Ingestion Most data use-cases depend on ingesting documents, so we built a state-of-the-art document ingestion API to handle most data extraction use-cases from documents.
Common Document Ingestion Use Cases
Insurance
Extract liability and coverage details from ACORD forms
Retrieval Augmented Generation
Get high-quality layout-aware chunks from Documents for RAG and Knowledge Graphs
Legal
Analyze contracts, detect footnotes, detect presence and coordinates of signatures
Text Ingestion
Extract structured data from emails, CSVs, and HTML files