Case Study | Stealth Company

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Many legal firms still rely on manual entry to process scanned or digitized court documents. It is a time-consuming and error-prone task. Even a small mistake, like entering the wrong number of charges, can have serious consequences in legal workflows and reporting. Our client needed a way to automate metadata extraction and validation at scale, while ensuring compliance and accuracy across their documents.

The Challenge

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The project presented further challenges:

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Critical metadata such as the number of charges, dates, or legal section references were often misrecorded, impacting case accuracy and reporting.
Legal documents came in various layouts and styles, making it hard to apply consistent validation rules.
The legal team wanted to eliminate routine data tasks while maintaining full traceability and trust in the output.

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The Solution

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To address these challenges, our team adopted a strategic and methodical approach:

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The system performed semantic analysis of legal texts, allowing it to understand the structure, hierarchy, and meaning within complex court documents.
It automatically extracted essential metadata such as the number of charges, legal codes referenced, involved parties, and court dates.
Built-in error detection flagged common mistakes in manually entered metadata and suggested accurate corrections based on context.
The extracted data was formatted as clean, structured JSON, ready for integration with internal legal databases or reporting systems.
A feedback mechanism allowed the system to continuously learn from manual corrections, improving its accuracy over time.

The Results

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We achieved several notable outcomes:

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Over 90% accuracy in identifying and extracting critical metadata from court documents.
100+ hours saved each month in paralegal labor, freeing up staff for higher-value work.
Improved trust and traceability, ensuring legal metadata is consistently verified and audit-ready.

This solution provided the legal team with a reliable and scalable way to manage document metadata, improving accuracy, saving time, and reducing the risks of manual error in sensitive legal workflows.