Agentic Table Merging

Tensorlake’s Agentic Table Merging reconstructs these fragments into a single coherent table by reasoning over content and context, not just geometry. It handles both cross-page and same-page merges, even with noisy headers, footers, and multi-column layouts.

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Building HackerNews Podcast Generator with Gemini 3, Elevenlabs

Learn how to build a simple podcast generator that turns Hacker News posts into short audio summaries using a single Tensorlake Application.

Agentic Chart Extraction

Tensorlake’s Agentic Table Merging reconstructs these fragments into a single coherent table by reasoning over content and context, not just geometry. It handles both cross-page and same-page merges, even with noisy headers, footers, and multi-column layouts.

OpenAI GPT-5.2 Codex vs. Gemini 3 Pro vs Opus 4.5: Coding comparison

With new AI coding models like Anthropic's Claude Opus 4.5, Google's Gemini 3 Pro, and OpenAI's GPT-5.2 Codex releasing rapidly—each claiming top benchmarks and massive context windows—developers struggle to choose the best daily driver. This comparison tests them on real-world tasks: creating a Pygame Minecraft clone, cloning a complex Figma dashboard, and solving a hard LeetCode problem, assessing their performance in UI/frontend work, general coding, algorithmic reasoning, efficiency, and reliability.

Gemini 3 OCR - Quick Findings

While Gemini 3 can read PDFs and generate HTML, the output often lacks structure and requires additional cleanup and formatting to be truly useful. Tensorlake supports precise page range selection and automatically produces organized, structured JSON with labeled fragments, grouped layout elements, and well formatted tables. This leads to a smoother developer workflow through a single API, with ready to use document data and no need for extra post processing or prompt tuning.

How Tensorlake Solved the DOCX Tracked Changes Problem for Legal Tech

Legal AI teams have struggled for years to parse DOCX files with tracked changes without losing bounding boxes or revision history. Tensorlake DocumentAI solves this problem with a unified parser that preserves full audit trails, spatial metadata, and comments in a single API call.

Gemini 3 is Now Available as an OCR Model in Tensorlake

Starting today, you can start using Gemini as an OCR Engine with Tensorlake’s Document Ingestion API. You can ingest Documents in bulk, and convert them into Markdown, classify pages or extract structured data using JSON schema. Tensorlake will take care of queuing, working with rate limits and sending you webhooks as documents are processed.

Benchmarking the Most Reliable Document Parsing API

Learn how Tensorlake built the most reliable document parsing API by measuring what actually matters: structural preservation, reading order accuracy, and downstream usability. See benchmark results comparing Tensorlake to Azure, AWS Textract, and open-source solutions on real enterprise documents.

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.

Developer Stories

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.

Accelerate Advanced RAG with Tensorlake

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.

Page Classification: Smarter, Safer Structured Extraction

Extract the *right* structured data *from the right pages*, with zero extra complexity

Unlocking Smarter RAG with Qdrant + Tensorlake: Structured Filters Meet Semantic Search

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.

LangChain + Tensorlake: Unlocking Document Understanding for Agents

LangChain and Tensorlake join forces to enhance agent-driven workflows with reliable document parsing and understanding.

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.

Get server-less runtime for agents and data ingestion

Data ingestion like never before.

TRUSTED BY PRO DEVS GLOBALLY

Tensorlake is the Agentic Compute Runtime the durable serverless platform that runs Agents at scale.

"For BindHQ customers, the integration with Tensorlake represents a shift from manual data handling to intelligent automation, helping insurance businesses operate with greater precision, and responsiveness across a variety of transactions"

Cristian Joe

CEO @ BindHQ

“Tensorlake let us ship faster and stay reliable from day one. Complex stateful AI workloads that used to require serious infra engineering are now just long-running functions. As we scale, that means we can stay lean—building product, not managing infrastructure.”

Arpan Bhattacharya

Founder & CEO @ The Intelligent Search Company