Find & Fill Gaps in Your Data

Software today can taste beer, beat us at chess, predict how proteins fold, and create hit music. These are hard problems, and the complex algorithms that solve them weren’t written by you and I, they were written by machines. Its likely that most code will be written by machines someday (they’re really quite good at it), but before machines can design solutions for us, we need to teach them about our world.

With data, we allow machines to observe what we observe. That’s why tomorrow’s problems won’t be solved with the right syntax, they’ll be solved with the right data. Data is our new, universal, programming language. But knowing what to say and when to say it is never easy - and data is no different. Every machine learning engineer will tell you that their biggest problem is obtaining high-quality, algorithm-ready data. Most of the time its impossible to know what ‘high-quality’ even means.

At Refine, we show you exactly which of your data you should use when building a machine learning solution. And if you’re missing the right data or need more of it, we’ll generate it for you. We do this because bad data has cost us time, money, customers, and the hair on our heads. We’ve struggled time and time again trying to figure out what ‘good data’ looks like and where to find it. For us, as machine learning developers, this is personal.

If you work with data, then you’ve likely faced similar circumstances. Chances are its personal for you too. When you give us your email, you give us a chance to reach out and learn your story. So join our waitlist. Chat with us. Together, we’ll figure out how to teach machines to solve all our problems, no matter how crazy.