StractusAI
Structured data from unstructured mess
StractusAI launched on April 23, 2025 with 13 votes, #2,155 of 7,818 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
StractusAI helps engineers and professionals extract clean, structured data from documents using the power of LLMs (OpenAI, Anthropic, etc). Just define your schema, upload your file and get ready-to-use data in seconds.
Implementação de automação com IA para empresas de serviço no Brasil. Um processo travado vira sistema em produção em 14 dias, com simulação antes de tocar em cliente.
Um processo do seu negócio, o que trava e custa dinheiro todo mês, sai do papel e entra em produção em 14 dias. E nada toca no seu cliente antes de você ver o ciclo inteiro rodando em simulação e aprovar. O orçamento que demora três dias pra sair. O lead que perguntou no sábado e foi respondido na terça. A cobrança que ninguém tocou esse mês porque a pessoa que fazia saiu. Isso não aparece no balanço como prejuízo. Aparece como contrato que não fechou, cliente que não voltou e hora de gente boa gasta em trabalho que uma máquina fazia melhor. E o pior: você já sabe qual é o processo. Consegue apontar com o dedo. O que você não sabe é se dá pra resolver com IA, o que trava no seu sistema, e…from stractusai.com
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Documind – Open-source AI tool to turn documents into structured data2024 · github.com · ▲169Documind 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.
NinjaDoc Ai2025 · 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…
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I just open sourced my document/website extractor for Vision-LLMs2024 · github.com · ▲37Hi 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…
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