Dev tools · alternatives · 2026
24 alternatives to Tile.run – Extract structured data from any document via API
Hey HN, Today, we’re launching tile.run, an API that extracts structured data from unstructured documents (PDF, images, text) with support for custom schemas. The Problem: Extracting data out of unstructured documents is surprisingly hard. We built tile.run while solving this for our product Kili (automation for invoicing/reconciliation). We found that getting to accuracy that is reliable enough for automation is challenging. Dense documents (e.g., lots of tables or line items) are even harder, and these are the most valuable to automate. After talking to other teams and developers, we…
Tile.run is an API that extracts structured data from unstructured documents including PDFs, images, and text files. Users define custom schemas to specify what data they need extracted, and the service handles complex… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier. Tile.run – Extract structured data from any document via API launched in 2024; newer entries below may have overtaken it.
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Documind is an open-source tool that turns documents into structured data using AI. What it does: - Extracts specific data from PDFs based on your custom schema - Returns clean, structured JSON that's ready to use - Works with just a PDF link + your schema definition Just run npm install documind to get started.
2024 · github.com · its alternatives →
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Context.dev▲814Hello, My name is Yahia, I've been working on scraping products for a long time and maintain a couple of free sites in my spare time (E.g. knifegeek.io) What prompted this is i've rebuilt scraping infra at every company I've started and finally just made it a product. Pass a URL, get clean markdown, html, images, sitemap or pass a domain, get logos, colors, socials, industry data. Goal is to offer a better service than VC funded competitors at a fraction of the cost. Stealth is included and automatic at no additional cost (normally an upcharge by alternatives). Would love your honest thoughts.
Jul 2026 · context.dev · its alternatives →
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Transform unstructured data into actionable data, instantly
2024 · portday.com · its alternatives →
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DataSieve 2.0▲119Extract structured data from text, files and archives.
Mar 2026 · apps.apple.com · its alternatives →
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Extract web data into structured JSON, no scraper required.
Jun 2026 · tabstack.ai · its alternatives →
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Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground
2023 · automorphic.ai · its alternatives →
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Turn any website into an API (for those who miss Kimono)free tier, from $19/mo▲332Web scraping made simple — extract data with our free Chrome extension and powerful cloud platform. Turn websites into structured data at scale, via dashboard or API.
2019 · simplescraper.io · its alternatives →
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Secure on-device extraction even if you're offline
2018 · veryfi.com · its alternatives →
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Trellis AI▲151Let swarm of agents automate your most manual PDF tasks
2025 · runtrellis.com · its alternatives →
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Search, visualize and analyze spatial and unstructured data
2022 · portal.unstruk.ai · its alternatives →
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Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…
2024 · github.com · its alternatives →
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Extract data from documents with AI using our tool, Extracta LABS. Automate document data extraction using an AI image data extractor.
2024 · extracta.ai · its alternatives →
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PDF Dino▲155Extract structured tables from messy PDFs using AI. Export to Excel, CSV, or JSON. Try 20 pages free—no credit card needed.
2025 · pdfdino.com · its alternatives →
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Pibit.ai▲107API to turn unstructured documents into structured data
2021 · pibit.ai · its alternatives →
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NinjaDoc Ai▲90Hey HN, I'm the founder of Ninjadoc AI. I've spent years working with document processing, and I've always been frustrated by the existing solutions for structured data extraction. The core problem is that most tools force you into one of two bad options: Template-based extractors: You define fixed regions or rules. These are incredibly brittle and break the moment a document layout changes slightly (e.g., a new invoice template from a vendor). ML-based extractors: These require you to gather hundreds (sometimes thousands) of your own labeled documents to train a custom model for each…
2025 · ninjadoc.ai · its alternatives →
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Batch extract structured data from unstructured sources.
2025 · extractany.com · its alternatives →
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Hey HN, It will be more of a "How I released my API without managing a website, servers, users, and payments. With 0$ up-front cost" Over the past year, I have come up with a plan of how I could release my own product without having to deal with managing users and/or dealing with payment processing. It is a 3 steps procedure: 1. Make an API that solves a problem 2. Deploy it with a serverless architecture 3. Distribute through an API Marketplace That took me about 2-3 days to develop an API using Flask, deploy it via Zappa on AWS, and release through RapidAPI. Source code of API:…
2020 · its alternatives →
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Hello HN! One of the most common uses of LLMs is to go beyond what traditional RPA or IDP can do when it comes to structuring unstructured documents. However, there are a lot of challenges in getting this done right from extraction of text data from PDFs, scanned images or other formats, prompt engineering, evaluation and integration with existing systems. This very specific use case is where Unstract can help teams move really fast, leveraging LLMs. By doing the heavy-lifting in this fast-changing ecosystem it lets engineers concentrate on implementing core business workflow automations.…
2024 · github.com · its alternatives →
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Hey HN, I'm Adithya, a 20-year-old dev from India. I have been working with GenAI for the past year, and I've found it really painful to deal with the many different forms of data out there and get the best representation of it for my AI applications. That's why I built OmniParse—an open-source platform designed to handle any unstructured data and transform it into optimized, structured representations. Key Features: - Completely local processing—no external APIs - Supports ~20 file types - Converts documents, multimedia, and web pages to high-quality structured markdown - Table extraction,…
2024 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →