AI · alternatives · 2026
24 alternatives to StractusAI
Structured data from unstructured mess
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
- 1
- 2
- 3
DeepTagger▲386Hi HN, I built a tool for teaching LLMs how to extract structured data from documents by annotating, not prompt engineering. I’d love your feedback. How it works: - Upload a document (DOCX, PDF, image, etc.) - Select and tag parts of it (supports nesting, arrays, custom tag structures) - Upload another document → click "predict" → see editable annotations - Amend them and save as a new example - Call the API with a third document → get JSON back Use cases: - Identify "important clauses" in contracts - Extract "total value" from invoices - Anything subjective, like "healthy ingredients" on a…
2025 · deeptagger.com · its alternatives →
- 4
Koncile ▲251Customisable OCR for all your data extraction needs
2024 · koncile.ai · its alternatives →
- 5OS
Complete AI Platform: RAG Systems & Intelligent Agents for Local AI
2025 · rlama.dev · its alternatives →
- 6
Struct▲329Hi HN! I’m Jason, a product designer at Struct Chat. At Struct, we're frustrated by the clutter, distractions, and inefficiencies plaguing existing chat platforms like Slack and Discord. We've built a radical new chat platform that leverages threads, feeds, and AI to help alleviate these problems, and give you back more time in your day. Struct uses a thread-first approach to keep conversations on-topic. It applies AI-generated titles and summaries to help you decide what's worth your attention. Threads are then organized in a real-time feed, keeping all your conversations up-to-date and at…
Mar 2026 · struct.ai · its alternatives →
- 7
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 →
- 8
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 →
- 9
- 10
Your automated threat intelligence analyst. Extract machine readable intelligence from unstructured data.
2021 · stixify.com · its alternatives →
- 11
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 →
- 12
Turn any webpage into structured data using LLMs. Contribute to mishushakov/llm-scraper development by creating an account on GitHub.
2024 · github.com · its alternatives →
- 13
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 →
- 14
DataSieve 2.0▲119Extract structured data from text, files and archives.
Mar 2026 · apps.apple.com · its alternatives →
- 15
Pibit.ai▲107API to turn unstructured documents into structured data
2021 · pibit.ai · its alternatives →
- 16
folio▲74hi HN! a couple of months ago I had to analyze a few thousand audio recordings to help identify issues with customer support. i was able to get some raw high-level initial results with python scripts invoking LLM APIs, but they were too general and unhelpful. writing basic prompts is easy, but tuning them and making them specific enough to ensure no faint signal is missed is hard. you need to iterate through the data with an initial prompt, segment the data into different buckets, chain another prompt for each bucket etc. Then you need to constantly review the raw data to tweak the prompts…
Apr 2026 · usefolio.ai · its alternatives →
- 17
We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com · its alternatives →
- 18
Extract and process unstructured data with ease.
2017 · its alternatives →
- 19
DocumentAI▲30Process any document to get structured JSON output using AI
2024 · finigami.com · its alternatives →
- 20
Extract structured data from unstructured documents with AI
2025 · app.unstructure.ai · its alternatives →
- 21
Hi HN! I built Docuglean, an open-source SDK for intelligent document processing that works with OpenAI, Mistral, Google Gemini, and Hugging Face models. The idea came from repeatedly writing boilerplate code to extract structured data from invoices, receipts, and other documents. Instead of wrestling with different API formats, I wanted a unified interface that: - Extracts structured data using Zod/Pydantic schemas - Classifies and splits multi-section documents (e.g., medical records) - Processes documents in batches with automatic error handling - Works locally without APIs (for…
Nov 2025 · github.com · its alternatives →
- 22
I built a CLI tool in Go that extracts structured data (JSON, CSV, Parquet) from messy PDFs and HTML pages. The core idea: LLMs are great at understanding structure but wasteful for bulk data extraction. So smelt uses a two-pass architecture: 1. A fast Go capture layer parses the document and detects table-like regions 2. Those regions (not the whole document) get sent to Claude for schema inference — column names, types, nesting 3. The Go layer then does deterministic extraction using the inferred schema This means the LLM is never in the hot path of actual data processing. It figures out…
Mar 2026 · github.com · its alternatives →
- 23
Batch extract structured data from unstructured sources.
2025 · extractany.com · its alternatives →
- 24
Myparser AI▲25Extract structured data from documents in minutes
2025 · myparser.ai · its alternatives →
Also compare
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 →