How does it work?

We use machine learning to validate data coming into your platforms

You can customise the filters to meet your exact business rules and specify how your datasets should fit together and then you can integrate them into your data pipeline. We use AI to correct errors in any incoming datasets and if the errors cannot be resolved the data will be quarantined and you will be alerted


Set-up your filters

Upload an example file here and customise the business rules you want to apply


Integrate our API

Easily implement our Python API into Airflow, Celery, or whatever tools you are using to build your data pipeline


Get clean data

Companies that have integrated Spotless into their workflow report substantial reductions in the costs of managing their data

Fast, Easy and Reliable Quality

With more data flowing across organisations it’s increasingly important to ensure that only reliable high quality data is present in your data platforms. Spotless sits inline with your data pipeline ensuring data quality before the data hits your systems.

Our configurable business rules can be set up to automatically correct errors in the data, compare multiple datasets for consistency and only let data through when it meets every criteria you’ve set up.

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Why are people using Spotless?


The costs of data quality

  • Solve problems before they occur without having to run daily reports and checks
  • Focus your operations teams on what is core to your business and let us fix your data quality

Keeping the data flowing

  • Stop your data pipeline going down every time it receives data that doesn’t meet the expected specifications
  • Speed up data loading by moving all syntax checking and validation to Spotless

Real Business Intelligence

  • You can’t make good decisions with bad data. Spotless makes bad data a thing of the past
  • Get your data scientists focussed on solving business problems not fixing data mistakes

Our team of six staff running daily checks on data quality was reduced down to one person after implementing Spotless

"We were over-reporting consumption by 4% because of overlaps in third party metadata that caused double counting"

"Our data pipeline stopped processing twice a week before Spotless started to quarantine the bad data that was causing the crashes"

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The Latest Info


Find out what we've been up to over the last few weeks.