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Let AI detect your duplicates, clean, complete, and enrich your data within a single solution.


    Increase your data quality

    Find all the data quality features:

    • Deduplication
    • Outlier detection
    • Normalization on a sizable functional perimeter (address, bank details, customer details, etc.)
  • Leverage the power of machine learning

    Let AI adjust processing to your context and optimize your automatic processing with the annotation tool.

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    Turn your data into opportunities: enrich it with other sources of information (internal data, open data, etc.) to better target your prospects or interventions, imagine new offers, develop operational performance, etc.

Have a look at Smart Data Quality

Mobilizing your data has never been easier

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    Better accuracy

    Diagnostic accuracy of 95%.

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    Optimized integration

    Data quality processing is generated in a few clicks and natively integrating your business rules

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    A full interfacing service

    Compatible with your existing tools



    We cleaned up the customer database as part of a CRM migration by deploying Smart Data Quality, a solution by Sia Partners, on AWS Cloud.


    We enriched customer data with Open Data to refine customer segmentation.


    We enriched their data with those of social landlords to identify social housing and prioritize the maintenance of connections on its network.


    We enriched customer data with median incomes, household composition, and garden sizes to better understand water consumption and design new service offerings accordingly.

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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.


Read more
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?


Read more