Precise Data Extraction: Pattern-Based Partitioning for Structured Extraction
Stop wrestling with brittle document extraction pipelines that break when layouts change. Learn how Tensorlake's pattern-based partitioning to extract data from specific document sections, eliminating positional dependencies and parsing noise for consistent structured outputs.
Building Clean, Schema-Enforced Pipelines with Tensorlake + Outlines
Learn how to build bulletproof document AI pipelines by combining Tensorlake's structured parsing with Outlines' schema-enforced generation. This technical guide shows how to eliminate malformed JSON, validation errors, and downstream failures by constraining LLM outputs during decoding rather than hoping for valid results.
Citation-Aware RAG: How to add Fine Grained Citations in Retrieval and Response Synthesis
Learn how to build citation-aware RAG systems that link AI responses back to exact source locations in documents. This technical guide covers document parsing with spatial metadata, chunking strategies for preserving citations, and implementing verifiable AI responses with page numbers and bounding box coordinates. Includes code examples using Tensorlake's Document AI for parsing complex documents and generating audit-ready citations in production RAG applications.
Parse and Retrieve Dense Tables Accurately with Tensorlake
Learn how Tensorlake preserves structure in dense, multi-page tables—returning DataFrames with summaries and bounding boxes for accurate, explainable retrieval.
Verify Structured Output with Field-Level Citations
Tensorlake now supports citations in Structured Extraction. Every extracted field can be traced back to its bounding box and page number—unlocking auditing, compliance, and verification workflows.
Fix Broken Context in RAG with Tensorlake + Chonkie
RAG pipelines fail when contracts, financial reports, or research papers are split into meaningless chunks. Learn how Tensorlake’s parsing and Chonkie’s chunking work together to deliver faithful, retrieval-ready context.
Advanced RAG that survives production: keep context fresh, preserve structure, and plan retrieval using Tensorlake to turn messy PDFs into traceable answers. We demonstrate it by fact-checking Tesla news against SEC filings.
AI Tagging for Page-Level Metadata with Tensorlake Page Classification
Learn how AI Tagging with Tensorlake’s Page Classification turns unstructured documents into page-level metadata for CRMs, vector databases, RAG pipelines, and compliance workflows—enabling precise search, automation, and structured data extraction.
A modern RAG stack demands more than vectors. In this post, we show how to combine Qdrant and Tensorlake to build smarter retrieval pipelines with structured filters, figure/table summaries, and markdown chunks enriched with document metadata. Learn how to parse research papers, create embeddings, and answer nuanced queries using real-world document structure, no fragile pipelines required.
Signature Detection in Tensorlake: Catch what’s missing, trigger what’s next
Signature Detection is now available in Tensorlake. Automatically identify whether a document has been signed—and use that signal to power intelligent automations.
Tensorlake Cloud: Ingest, Structure, Orchestrate Without Losing a Byte
Tensorlake Cloud is a fully managed platform for turning unstructured documents into structured, AI-ready data. With human-like document parsing and code-first workflow orchestration, delivering the accuracy and durability needed for high-stakes applications in finance, healthcare, and more.
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