Data Transformation Engine

Map, Transform, and 

Validate data 10x faster

Go from messy to clean data in minutes with Generative AI
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A transformation engine
built for messy data
Clean and validate even the messiest data at ingestion
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Go from messy data to clean data in seconds...
Instantly reduce data cleanup with automatic mapping and natural language cleanup
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with a system that learns and adapts...
Our powerful generative AI learns and adapts to your data, use case, and workflow
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while maintaining control of data quality.
Empower engineering teams to build testable data quality guarantees withfull data lineage

Supercharge your data team

Amplify your team's efficiency with end-to-end,adaptive data ingestion
Fast, east data cleanup.
Uncompromised data quality.
Discover an easier and more powerful way to ingest data
Don't let bad data in

Enforce syntactic, semantic, and business rules at ingestion with out-of-box validation and Webhooks.

Let anyone fix data errors

Gen-AI powered AutoMapping learns from your historical uploads and automatically maps 100s of fields instantly.

Natural language-based cleanup

Execute complex cleanup tasks and clean the messiest data like a pro. Simply tell us how it should be done using everyday sentences.

Automatic Value Matching

Generative AI to semantically map data to your standardized list of values

Spreadsheet-style formulas

With AutoClean you get the convenience of AI-powered solutions while retaining the control and flexibility of formulas when you need them.

Webhooks for Maximum Flexibility

Handle your most challenging transformations through easy code extensibility

Single-click lookups

Stop fumbling with VLOOKUP functions and easily perform AI-powered lookups with the touch of a button

No-code joins

Now any team member can join together multiple, disparate datasets without the need for SQL.

SQL-free aggregations

Execute complex data combinations and calculations (i.e. SUM, AVG) on incoming data using natural language, no SQL knowledge required.

Quickfix lookup operation in Osmos. The form includes dropdown selections for the dataset (“Clothing_promos”), table (“Spring_clothing_line”), lookup field (“product_line”), and matcher field (“product_ID”). The matcher field is used to align records between datasets, enabling accurate data retrieval based on common values. The UI is structured for easy selection and setup of lookup relationships.
The Query Builder interface for selecting and joining tables in Osmos datasets. The UI displays a list of tables and their respective joins, including “product_line” as the primary table. “Inventory” and “Promotions” are joined to “product_line” using a left outer join, while “Sales Data” is joined to “Promotions” with a left outer join. The layout allows users to dynamically add tables and define relationships for querying structured data.
The Osmos user interface displaying the “Group By” selection tool and a table summarizing promotional types. The “Group By” section shows “Promo_type” as the selected field. Below, a table lists different promotion types, such as “Buy One Get One Free,” “Percentage Off,” “Bundle Deal,” “Loyalty Rewards,” and “Limited Time Flash Sale,” along with their respective counts. The interface is designed for aggregating and analyzing data efficiently.

Now anyone can transform data like a pro

Go from messy to clean data in minutes with Generative AI
Talk to an expert
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Solving the first mile of data ingestion
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– Uploader– Pipelines– Data Transformation– Datasets
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