Value estimation

refinery is designed for data scientists that get serious about the quality of their training data. Think of it as a data quality assistant that helps you to improve your data quality and quantity while saving time and money. How much? Let's find out.

If you have more use cases, or multiple prediction tasks per use case, you can multiply the gained value with these factors. For this calculation, think of an exemplary use case in your team.

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Your benefits

With refinery, you're able to gain the following benefits:
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    What refinery can do for you.

    The central application for your data scientists.

    • Optimize annotation spend

      Save time and money, especially when time of domain experts is a scarce resource. Automate what is possible, and find rare cases that need human attention.

    • Shorten model development time

      Our users have been able to prototype complex models within an afternoon, just by scaling their training data. Bring your models to market faster with us.

    • Debug and improve your model

      Modern algorithms are blackboxes. Find their weaknesses in a data-centric manner, and improve your model by fixing that data or creating new slices for re-training.

    • Collaborate with domain experts and annotators

      It has never been easier to integrate domain expertise into your work specifically on the data you need help for. Just send them a link, or tell them to sign in.

    • Slice and explore your training data

      Find insights about your data, integrate your existing models for benchmarking, and use neural search to find both similar examples and outliers. This is your navigation system.

    • Integrate with your existing workflow

      If you have an annotation workflow up-and-running, you do not need to kill it. Just use our Python SDK to integrate it into your existing workflow, and gain all the benefits of refinery.