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Semantic Search Engine for Documents

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    Year of implementation
    2022
    Industry
    Professional Services

    Internationally recognized law firm, specializing in high-quality legal consulting and comprehensive services for businesses & institutions

    Artea developed an advanced semantic search engine for an international law firm, capable of indexing hundreds of thousands of legal documents. The solution combines automated indexing technologies, LLM-based tools for content analysis, and the open-source search engine Apache Solr, ensuring fast, accurate, and relevant searches within a constantly updated knowledge base.

    Approach and methodology

    The project involved the development of an advanced semantic search engine for an international law firm, capable of managing and indexing hundreds of thousands of legal documents. The solution is based on an indexing framework with automated processes, ensuring the knowledge base is constantly updated and information is immediately available.To optimize content analysis, the system integrates a Large Language Model (LLM) that automates the interpretation and classification of documents, improving the accuracy and speed of searches. Indexing is handled by Apache Solr, an open-source engine known for its speed and reliability in managing large volumes of data. Thanks to this approach, the law firm can quickly retrieve relevant information, reduce research times, and more effectively support advice and strategic decisions, increasing operational efficiency and the quality of service provided to clients.

    We developed a comprehensive solution based on an advanced indexing framework with automated process management to ensure the knowledge base remains continuously updated. The system leverages a Large Language Model (LLM) to automate complex document analysis tasks and utilizes Apache Solr, a powerful open-source search engine, for high-speed, preci

    Staff involved in the project
    3 Specialists
    Execution time
    4 Months
    Technologies used
    • BERT
    • OpenAI
    • Solr
    500
    Number of indexed documents
    50
    ms
    Average response time
    70
    %
    Accuracy

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