Launch HN: Midship (YC S24) – Turn PDFs and Images into usable data
Midship, founded by Max, Kieran, and Aahel, specializes in extracting data from unstructured documents using OCR and language models, catering to both non-technical users and developers with subscription and API pricing.
Midship, founded by Max, Kieran, and Aahel, specializes in extracting data from unstructured documents such as PDFs and images. Initially, the team aimed to create a natural language workflow builder as an alternative to platforms like Zapier. However, they pivoted to focus on document extraction after observing significant user interest in this feature. Many businesses still rely on PDFs and images for data, and while OCR technology exists, it often fails to provide clean, structured data. Midship addresses this issue by combining OCR with language models, enabling the extraction of specific fields and tables while correcting OCR errors and understanding context. The platform caters to two main user groups: non-technical users who utilize the web app for data extraction and developers who access the extraction API. Midship offers a subscription model for the web app and volume-based pricing for the API. The founders are eager to receive feedback from the community on their product.
- Midship focuses on extracting data from unstructured documents like PDFs and images.
- The platform combines OCR with language models to improve data extraction accuracy.
- It serves both non-technical users through a web app and developers via an API.
- The company offers a subscription model for the web app and volume-based pricing for the API.
- Feedback from the community is welcomed as the founders continue to develop their product.
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20 commentsI want to create something that can paginate through 12 pages of html, perform clicks, download pdf fund factsheet, extract data from this factsheet into excel or CSV. Can this help? What's the best way to deal with the initial task of automating webpage interactions systematically?
Tried the examples - they seem tailored for specific document types. I have two questions around that: (a) is their a "best-effort" extraction you can perform or plan to support if you don't know the document type? (b) do you plan to support extraction from academic papers, i.e., potentially multi-column, with images, tables that are either single column or span two columns, equations, etc.?
Curious what are the biggest complain from your users? Are they willing to manually auditing the numbers in the table, make sure the output is 1. accurate. 2. formatted in the table they expected.
I’m curious to hear more about your pivot from AI workflow builder to document parsing. I can see correlations there, but that original idea seems like a much larger opportunity than parsing PDFs to tables in what is an already very crowded space. What verticals did you find have this problem specifically that gave you enough conviction to pivot?
Can you do this with emails?
- https://www.ycombinator.com/companies/tableflow
- https://www.ycombinator.com/companies/reducto
- https://www.ycombinator.com/companies/mindee
- https://www.ycombinator.com/companies/omniai
- https://www.ycombinator.com/companies/trellis
At the same time, accurate document extraction is becoming a commodity with powerful VLMs. Are you planning to focus on a specific industry, or how do you plan to differentiate?
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