Imagine you are conducting due diligence or taking on a mandate with an extensive data room. You have a digital stack of 146 employment contracts in front of you. You know that there is one fixed-term contract among them that you need to identify. Manually, this would mean opening 146 PDFs, pressing CTRL+F, searching for “fixed-term,” sorting out false positives, and checking the context. This costs valuable hours.

With modern legal tech and AI-first approaches, this “needle in a haystack” can be found in seconds. We'll show you how this process works with PyleHound.

Key Takeaways

• Semantic intelligence: PyleHound understands legal contexts (e.g., “no unlimited term” = “fixed term” or “end date”) instead of just searching rigidly for keywords.
• Massive time savings: The analysis of 146 complex employment contracts takes just a few moments instead of hours of manual review.
• Precision: The AI accurately filters out the one relevant document from over 1,000 text passages.

How does semantic search differ from classic keyword search?

Semantic search is superior to classic keyword search because it understands the legal intent behind your question instead of just scanning for exact character strings.

When you ask PyleHound: “Which of the employment contracts does not have an indefinite term?”, the system does not just stubbornly search for these words. The AI recognizes the legal context (semantics). It knows that concepts such as:

• “fixed term,”
• “end date,”
• “ends on,” or
• “fixed-term employment”

are relevant indicators for your question. This is crucial because contracts often use different wording for the same concept. Simple keyword matching would fail here or deliver incomplete results.

How does the analysis process of large amounts of data work in practice?

The process is designed for maximum efficiency and user-friendliness and can be divided into four logical steps:

1. Import: You drag and drop the entire folder (e.g., 146 PDF files) into the PyleHound knowledge database.
2. Question: You ask your question in natural language, just as you would ask a colleague (e.g., about the term).
3. Quote scan: PyleHound searches all documents. In this case, the system first identified 1,143 relevant text passages that potentially refer to terms.
4. Intelligent synthesis: Instead of having to read through these 1,143 passages, you instruct the AI to use these citations to finalize the one document you are looking for.

How reliable is the result of the AI analysis?

The result is highly accurate because PyleHound combines semantic search and logical reasoning to exclude all 145 other standard contracts as “permanent.”

In the example shown, the AI filters the enormous amount of data and provides you with the exact file name as the result: “2023 Employment Contract Employer 130.pdf.” It justifies this directly with the decisive clause from the contract: “Your employment relationship is temporary and ends on...”.

This transforms a task that would have taken half a working day to complete manually into a process that takes just a few moments. This frees up time for the actual legal work: advising your clients.

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Want to speed up your due diligence processes? See how PyleHound can make your law firm more efficient.

Transcript

I received a folder containing a large number of employment contracts. I believe there are 146 of them. And I know that one of them has a limited term. We would like to locate that one now.

I'm adding the folder here and importing all 146 files from it.

I ask the question: “Which of the employment contracts does not have an unlimited term?”

And PyleHound has recognized this very nicely here. PyleHound has now directly identified the correct terms from my, let's say, somewhat amateurish query that would make sense in this legal context. PyleHound naturally starts a semantic search here. This means that PyleHound searches for terms that could have the same meaning. And that is, of course, “fixed term” in this case. That could be “end date” or “ends on.” This is precisely where the difference to the classic keyword search lies.

PyleHound tells me: I have searched for the relevant terms “fixed term,” “end date,” “fixed-term,” etc. And a large number of relevant text passages were found – 1143. And that is, of course, quite a lot. Now I can go through them and see: Is this a text passage that I want to include? Is this something that might not be important?

But I can also go one step further and say: I'll use all the selected quotes here for further processing and let PyleHound figure out which document is the right one.

Namely here: “Based on the selected quotes, the following employment contract is not permanent, but fixed-term.” And that's number 130 here. Needle in the haystack.