Analyse your PDFs with AI

Analyse your PDFs with AI

Reading through hundreds of pages of text in a PDF file to find that one thing you're looking for is time consuming. With our AI expert system, you can upload your PDF file to a password protected machine learning model, and start "asking questions" to your PDF files.

The process is easily explained:

  1. Upload the file to your cloudlet.
  2. Optionally preserve page numbers during import if you need page number references, and create a system instruction that returns the page number of where it found the information it used to answer your questions.
  3. Start asking questions to the PDF file using an AI expert system.

If you choose to preserve page numbers during import, the system will generate one training snippet for each page, and add the page number to the training snippet itself - Resulting in that as you phrase questions towards your PDF file(s), you can even prompt engineer it to return the page number of where it found the answer as a part of its response - Allowing you to look up and verify the answer the AI gives you. Below are some examples of things we've already done in this space.

Use case: Analysing scientific studies

A week ago we had a partner working as an earth scientist. His job was to analyse scientific reports provided to him as PDF files. He had hundreds of files that he wanted to break down into individual AI models, allowing him to phrase questions related to the data.

By creating one machine learning type for each individual file, he could rapidly phrase questions to his AI expert system, allowing him to "ask questions and have the PDF file 'answer'." Since these PDF files contained mathematical formulas, in addition to a lot of additional facts, he could ask questions such as for instance.

Compute the 0 to 0.5 km storm-relative helicity given numerical model data I will give you in my next question

For the record, I've got no idea what the above question even means. We're just delivering the AI parts of the equation 😂

Use case: Legal AI systems

You can also imagine a law firm uploading files, such as court cases, verdicts, laws, etc - For then to ask questions towards these. For legal documents we've already got a very good case study illustrating the idea. You can find it below.

The above case study is about HR and employment law in Holland, containing hundreds of different files - But you can also create smaller models to answer questions related to individual court cases, or individual laws and regulations. You can read more about how we can deliver AI chatbots for the legal industry below.

Use case: Analysing resumes

Another thing we've previously delivered is the ability to analyse resumes to find matching candidates for vacant positions. This allows you to rapidly short list candidates when you've got hundreds of resumes, and no time to read through them all.

There are probably few things in life less rewarding than reading through 200 resumes, to find 3 candidates you want to invite to an interview. Our AI systems can rapidly search through 200 CVs, and within seconds provide you with some 3 to 5 candidates that are a good fit to the job you're looking to fill.

For recruiting firms with thousands of candidates, the benefits should be obvious

The value proposition

Your time is precious - Too precious to spend it searching through hundreds of pages of text. Time is the value proposition we bring to the table. Basically, if you spend a lot of your day reading through PDF files, we can probably significantly reduce the time you spend doing this, freeing up time for you, such that you can focus on more important tasks.

Using AI to analyse PDF files should really be a "no-brainer". If you want to hear more about how we can help you here, you can contact us here.

Thomas Hansen

Thomas Hansen I am the CEO and Founder of AINIRO.IO, Ltd. I am a software developer with more than 25 years of experience. I write about Machine Learning, AI, and how to help organizations adopt said technologies. You can follow me on LinkedIn if you want to read more of what I write.

Published 4. Jul 2024

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