Both of these are critical components, especially in cases where the data is needed to train systems, such as in the case of autonomous services, where you seemingly can never have enough teaching data. The advantage of anonymized data goes beyond simply replacing a synthetic data set for an actual one they can also be used to augment a data set, or to fill in the gaps where the real-world data might be lacking. So Gretel set out to build a toolkit that would let any company build anonymized data sets for themselves, similar to what big tech companies use in their own data work. Gretel’s opportunity is one that many companies targeting the enterprise market have taken in the world of digital transformation: many organizations now have large engineering operations working on applications to run their businesses, but they still do not have the firepower of the world’s largest technology companies. At this collision course, we should create tools” to fix that. AI is on a collision course with privacy. “But now we are running into the bottleneck of the data. “Removing the bottleneck of compute is the problem we’ve solved, and we have created high-velocity development,” he said.
military and over the years.Īs data engineers, their priority is to be able to work with data easily and quickly, but as citizens of the world, they were unhappy with data protection implications.
The germination for Gretel.ai came out of the direct experiences of the three co-founders in work they did as cybersecurity specialists at a range of organizations including IBM, AWS, Netscout and the U.S. There are other solutions to address the same issue involving data encryption, although this can be a costly, time-consuming and resource-intensive approach that faces scaling challenges. The idea behind using synthetic data sets is that it lets an organization remove the risk of data leaking that might contain personal information or other kinds of sensitive data. Greylock led the company’s previous round in 2020, and the startup has raised $65.5 million to date.įrom what we understand, this latest round puts the company’s valuation at between $320-$350 million. The product - which is built as a SaaS product but can also be accessed via APIs - is still in beta but aims to be open to general availability later this year.Īnthos Capital is leading the round with Section 32 also participating alongside Greylock and Moonshots Capital.
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Gretel AI, which lets engineers create anonymized, synthetic data sets based on their actual data sets to use in their analytics and to train machine learning models has closed $50 million in funding, a Series B that it will be using to get the company to the next stage of development.
Now, a startup that is building tools to make it easier for engineers to implement the two simultaneously is announcing a round of growth funding to continue expanding its operations. Increasingly, conversations about big data, machine learning and artificial intelligence are going hand-in-hand with conversations about privacy and data protection.
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