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More relevant data sources and features significantly boost the accuracy and robustness of ML and AI models. And your internal data is just a tiny part of what’s relevant.
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Revolutionary simplified search & enrichment with only relevant ready-to-use ML features in just 20 minutes, eliminating weeks of manual data sourcing and tests.
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Automated data cleaning and feature engineering from external sources with Generative AI, removing the need for data engineering or ETL.
Solves common pitfalls with external data in ML pipelines by selecting only features which don't drift over time and improve the accuracy of the ML model, not just correlated with the target variable.
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Easily integrates with production ML pipelines via batch & RESTful APIs.
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01
More relevant data sources and features significantly boost the accuracy and robustness of ML and AI models. And your internal data is just a tiny part of what’s relevant.
02
Revolutionary simplified search & enrichment with only relevant ready-to-use ML features in just 20 minutes, eliminating weeks of manual data sourcing and tests.
03
Automated data cleaning and feature engineering from external sources with Generative AI, removing the need for data engineering or ETL.
04
Solves common pitfalls with external data in ML pipelines by selecting only features which don't drift over time and improve the accuracy of the ML model, not just correlated with the target variable.
05
Easily integrates with production ML pipelines via batch & RESTful APIs.
How this works
Upgini GenAI enriches any text fields with facts from external data sources and generates more accurate embeddings as ready-to-use numeric features for downstream ML models:
Identifies entities like product titles or places of interest (POI) names in the text to match facts from the sources.
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Finds facts for extracted entities, such as POI descriptions and footfall traffic statistics.
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Generates enriched embeddings for text fields with facts fetched from external sources.
Example
We want to improve the accuracy of an ML model that predicts the probability of product usage decline for a specific client (what is called an attrition or churn model).
For every client in a labeled training dataset, in addition to numeric ML features, we have transcriptions of all calls to support, the history of the client's chats with support, and purchased product reviews from the website.
Simply pass this text as columns in a labeled dataset during the search process, and Upgini will automatically enrich these columns with relevant external sources and generate Enriched embeddings.
Accuracy
Enriched embeddings from Upgini Generative AI are more accurate than embeddings from top commercial embedders, such as OpenAI’s ada-002.
Yes, we are open to adding new data sources based on our clients' needs. Please reach out to our customer support team with support request, and we will do our best to accommodate it.
The frequency of data updates depends on the specific data source. Some sources are updated daily, others weekly or monthly. You can always check update frequency for a data source right in a search results.
Yes, we understand and prioritize the importance of data privacy. Upgini supports the use of hashed individual IDs as search keys, such as SHA256 hashed email. This allows us to enrich your data without exposing sensitive client information.
Absolutely. Our search algorithm is designed to prevent data leakage, simply pass the date or date+time as one of the search keys in a labeled dataset. This approach ensures that only information known and available at the specified date is used in the ML & AI model training process, effectively avoiding future signals or data leakage.
Yes, we provide Service Level Agreements to our clients. These agreements include commitments to service uptime and data distribution drift. For more details, please contact our customer support team
No credit card required. No time limit on Free plan
Talk to a team of experts awarded Gartner Cool Vendor status, driving AI & ML data monetization innovations since 2015
