Tensorlake

Product Introduction

1. Tensorlake is a cloud-based platform designed for document ingestion and data orchestration, specializing in transforming unstructured data from real-world documents into structured formats optimized for AI applications. It combines human-like layout understanding with scalable Python-based workflows to process diverse file types, including PDFs, images, spreadsheets, and handwritten notes. The platform automates post-processing steps like chunking and preserves document structure, enabling seamless integration with retrieval-augmented generation (RAG) pipelines and business automation systems.
2. The core value of Tensorlake lies in its ability to bridge the gap between unstructured data sources and AI-ready data pipelines, ensuring high accuracy and scalability. It eliminates manual data preprocessing by offering APIs and serverless workflows that handle complex document parsing, extraction, and classification at production scale. This enables organizations to focus on deploying AI models rather than data preparation, reducing time-to-insight and operational costs.

Main Features

1. Tensorlake Document Ingestion API parses and structures data from any file type, including handwritten notes, PDFs, mixed-language documents, and nested tables, while preserving reading order and layout. The API supports batch processing of thousands of documents daily, with automated chunking and metadata retention for RAG optimization. Users can programmatically upload files, initiate parsing jobs, and retrieve results via JSON or markdown outputs.
2. Tensorlake Serverless Workflows enable the creation of Python-based data pipelines that scale dynamically from zero to millions of documents without requiring infrastructure management. These workflows support parallel processing of lists, dependency-driven task orchestration, and integration with external databases or LLM frameworks. They handle end-to-end data transformations, such as converting slides to structured text or enriching extracted data with external APIs.
3. The platform provides enterprise-grade scalability, processing over 100,000 documents per customer daily with latencies as low as 8 microseconds per event. It achieves this through a distributed architecture that eliminates reliance on external queues or map-reduce engines, ensuring cost efficiency even at petabyte-scale workloads. Security features include role-based access control (RBAC), data encryption, and audit logs for compliance with regulatory standards.

Problems Solved

1. Tensorlake addresses the challenge of extracting usable data from unstructured or semi-structured documents, which often require manual intervention or error-prone OCR tools. It solves layout preservation issues in complex files like tax audits, property deeds, and global trade paperwork, where traditional parsers fail to maintain context.
2. The platform targets developers, data engineers, and AI teams building RAG systems, business process automation tools, or data-intensive applications. It is particularly valuable for industries like legal, finance, and logistics, where high-volume document processing is critical.
3. Typical use cases include converting scanned invoices into structured JSON for accounting systems, preprocessing multilingual research papers for LLM analysis, and automating extraction of nested table data from financial reports. For example, users can deploy workflows to parse 10,000+ mixed-format documents hourly and feed results directly into vector databases like Pinecone or Weaviate.

Unique Advantages

1. Unlike generic document parsers, Tensorlake specializes in layout-aware extraction, accurately handling tables, handwritten text, and multi-column formats that confuse most AI models. It outperforms rule-based systems by using adaptive algorithms trained on diverse real-world document types.
2. The serverless workflow engine uniquely combines Python flexibility with automatic horizontal scaling, eliminating the need for Kubernetes clusters or server provisioning. Workflows can process 10,000+ invocations per second while maintaining state between dependent tasks, a feature absent in most FaaS platforms.
3. Competitive advantages include patented chunking algorithms that optimize document segmentation for RAG recall rates and a pay-per-use pricing model that scales cost-effectively from prototype to enterprise deployments. The platform also supports hybrid cloud deployments for air-gapped environments, a critical requirement for government and healthcare users.

Frequently Asked Questions (FAQ)

1. What document formats does Tensorlake support? Tensorlake processes PDFs, images (JPEG/PNG), PowerPoint presentations, Excel spreadsheets, Word documents, and scanned handwritten notes. The system automatically detects file types and applies appropriate parsing models, including OCR for low-quality scans.
2. How does scaling work for serverless workflows? Workflows scale automatically based on incoming document volume, using a distributed task queue that parallelizes Python functions across serverless workers. Users define data dependencies, and Tensorlake handles parallel execution of independent tasks while maintaining execution order for dependent operations.
3. Can Tensorlake integrate with existing LLM pipelines? Yes, the platform outputs structured JSON or markdown chunks optimized for embedding models and vector databases. Prebuilt connectors are available for LangChain, LlamaIndex, and major cloud providers, enabling direct ingestion into RAG pipelines without custom integration code.
4. How does Tensorlake handle mixed-language documents? The API employs multilingual NLP models that detect and process text in 50+ languages within the same document. Layout context is preserved across language boundaries, making it suitable for international trade documents or academic research papers with multiple language sections.
5. What security measures protect processed data? All data is encrypted in transit and at rest using AES-256, with optional customer-managed keys. RBAC allows granular permission settings per namespace, and audit logs record every API call and document access event for SOC 2 compliance.