AI · alternatives · 2026
24 alternatives to Transform unstructured docs to structured data
LLM-Powered document extraction & analysis tool
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Transform unstructured docs to structured data launched in 2024; newer entries below may have overtaken it.
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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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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 webpage into structured data using LLMs. Contribute to mishushakov/llm-scraper development by creating an account on GitHub.
2024 · github.com · its alternatives →
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Batch extract structured data from unstructured sources.
2025 · extractany.com · its alternatives →
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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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DataSieve 2.0▲119Extract structured data from text, files and archives.
Mar 2026 · apps.apple.com · its alternatives →
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Extract structured data from unstructured documents with AI
2025 · app.unstructure.ai · its alternatives →
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Hi, I'm building an open-source self-hostable document extraction tool powered by LLM. There are popular data extraction tools and OCR tools in the market. None of them are open source. Most accounting firms, law firms, insurance, back office, and real-estate folks would like to use a tool like this. You can add PDF documents, Images, and audio files and create columns to answer questions on documents or extract information into tabular format. Access to repo: https://github.com/harishdeivanayagam/rowfill Screenshots:…
2025 · github.com · its alternatives →
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2023 · app.refuel.ai · its alternatives →
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Transform any document, web page, or eBook into a research paper (ChatGPT not required) - jstrieb/paperify
2023 · github.com · its alternatives →
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How to think about data
2015 · amazon.com · its alternatives →
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ExtractBench - A Benchmark for Schema-Guided Enterprise Document Extraction - run-llama/ExtractBench
Aug 2026 · github.com · 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! After struggling with complex prompt engineering and unreliable parsing, we built L1M, a simple API that lets you extract structured data from unstructured text and images. curl -X POST https://api.l1m.io/structured \ -H "Content-Type: application/json" \ -H "X-Provider-Url: demo" \ -H "X-Provider-Key: demo" \ -H "X-Provider-Model: demo" \ -d '{ "input": "A particularly severe crisis in 1907 led Congress to enact the Federal Reserve Act in 1913", "schema": { "type": "object", "properties": { "items": { "type": "array", "items": { "type": "object", "properties": {…
2025 · l1m.io · its alternatives →
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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 →
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2014 · docbag.org · 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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The traditional pipeline for unstructured data extraction typically follows these steps: 1. Image → OCR Model (e.g., Google Vision) → Layout Model (e.g. Surya) → LLM → Final Answer However, this can be streamlined using a Vision-Language Model (VLM): 2. Image → VLM → Final Answer Recently VLMs have improved a lot for OCR and document understanding tasks, specifically the Qwen-2.5-VL series. We can run the Qwen-2.5-VL-7B-AWQ model locally with just 16GB VRAM, and perform end-to-end information extraction (fields and table extraction) without any external models. Hallucination with VLMs One…
2025 · github.com · its alternatives →
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I built Sculptor after repeatedly seeing founders try to hire data scientists for a task that ultimately boiled down to extracting structured data from unstructured text (customer records, social posts, websites, etc) using an LLM API. We ended up reinventing this pattern internally at least three times in the past year, so I published Sculptor as a streamlined, open-source solution: - Simple schema-based extraction, with parallelization and type validation. - Multi-step pipelines with filtering or transforms between steps. - Configure everything in YAML/JSON for easy reuse. It’s MIT…
2025 · github.com · its alternatives →
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Improved file parsing for LLM’s. Contribute to Filimoa/open-parse development by creating an account on GitHub.
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 →