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Via a simple prompt interface, SiaGPT allows you to extract relevant information and generate unique content (insights, comparison…) based on your selection of a vast number of internal or external documents within your secure environment.


    Key Benefits

    Search only the documents you choose to upload

    Use capabilities from multiple LLMs in multiple languages

    SiaGPT interface desktop
    Start chatting with SiaGPT instantly No model training required Challenge the responses by tracking precisely the sources Rely on a secure solution accessible in SaaS or in your cloud
  • Main features

    Upload your documents / connect to your database and share your project internally

    Manage, extract and compare information from the documents uploaded

    SiaGPT interface desktop
    Chat with SiaGPT using the prompt to ask questions Generate insights and comparative results Trace the information by accessing the precise sources of the answers provided by SiaGPT in one click

  • Increase productivity in data collection and reporting

    Find information quickly within numerous documents

  • Develop an easy first use case for Generative AI

    Streamline the management of your supplier contracts

  • Compare your metrics against peers or competitors

    Summarise large volumes of information

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Our publications


Boosting search engine capabilities of RegReview:…

RegReview is an AI solution for compliance teams, to automate regulatory monitoring and processes, which brings together several tools, the most essential of which is a search engine operating on a compiled database of custom-built regulatory sources.

The database contains ~300k documents.


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Article title

Labeling text clusters with keywords

We propose to explore several keyword extraction techniques to label text clusters obtained after a Text Clustering or a Topic Modeling pipeline. This work is following our previous articles about Topic Modeling and Text Clustering (here and here).


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Synthetic data or how to share sensitive data…

For a period of six months, 5 students from Centrale Supélec and ESSEC worked collaboratively with Sia Partners on building a Python library to create fake - which we'll call synthetic - data.
But what's the point of creating fake data? How could it help organizations?


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