Alternatives
Products that do what DOXTRACT does
Structuring your Unstructured Data
- 1
- 2ZD
This started out as a weekend hack with gpt-4-mini, using the very basic strategy of "just ask the ai to ocr the document". But this turned out to be better performing than our current implementation of Unstructured/Textract. At pretty much the same cost. I've tested almost every variant of document OCR over the past year, especially trying things like table / chart extraction. I've found the rules based extraction has always been lacking. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. Using a vision model just make sense! In…
2024 · github.com
- 3
- 4
Affordable DocumentAI extraction starting from $1/1000 pages
Jun 2026 · soceton.com
- 5DO
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
- 6
- 7
- 8BT
2024 · github.com
- 9SO
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
- 10
- 11
- 12
- 13
Turn Unstructured Documents into Actionable Data with AI
Jun 2026 · sigixtract.com
- 14CA
Hi HN, author here. I built CommerceTXT because I got tired of the fragility of extracting pricing and inventory data from HTML. AI agents currently waste ~8k tokens just to parse a product page, only to hallucinate the price or miss the fact that it's "Out of Stock". CommerceTXT is a strict, read-only text protocol (CC0 Public Domain) designed to give agents deterministic ground truth. Think of it as `robots.txt` + `llms.txt` but structured specifically for transactions. Key technical decisions v1.0: 1. *Fractal Architecture:* Root -> Category -> Product files. Agents only fetch what they…
Dec 2025 · commercetxt.org
- 15WH
2024 · extracta.ai
- 16IJ
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
- 17
- 18
- 19GY
Hey HN! We're excited to announce the launch of Tonic Textual, the secure data lakehouse for LLMs. Simply stated, Tonic Textual allows you to build generative AI systems on your own unstructured data without having to spend time extracting and standardizing your data. In minutes you can build automated, scalable unstructured data pipelines that extract, centralize, standardize, and enrich data from your documents into an AI-optimized format ready for embedding, fine-tuning, and ingesting into a vector database. While in-flight, we also scan for sensitive information and protect it via…
2024 · tonic.ai
- 20UA
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
- 21
- 22TR
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…
2024 · tile.run
- 23
- 24TU
LLM-Powered document extraction & analysis tool
2024 · dataku.ai
Ranked by how close each launch is in meaning, then by votes. Refine with a description →