87% of AI projects never make it to production
. Whether it's because they are inaccurate, hallucinate, don't work in real-world contexts, misunderstand real people, or that they're stuck before they start because of the "cold-start problem”, the issue comes down to not enough high-quality use-case specific, privacy-compliant labeled data for training.
But real-world human-labeled data is expensive and requires a lot of data science labor – and synthetic data is so low-quality and unrelated to the model's actual use case that it doesn't improve performance much.
Nurdle can help. By using real-world human-labeled kernel datasets built for specific use cases, we produce enhanced synthetic datasets that perform almost as well as human-labeled at the speed and price of synthetic datasets. Contact us for a free data gap analysis showing you what kind of data you're missing and how much you need for your performance target.