When Julie Trias and Elizabeth Nammour worked together at Airbnb on the company’s data team, they had to deal with data that was scattered across multiple sources, and this growing spread led to data security challenges. The founders’ frustration with existing data protection options motivated them to start a company and create the automated data protection tool they wanted.
On Tuesday, this launch, Telescopeannounced a $5 million seed investment.
“We tried a bunch of different tools to help us understand, protect, erase and delete data at Airbnb, but what we realized is that there wasn’t a tool that could help developers do that automatically,” Trias told TechCrunch.
That’s not to say there weren’t solutions, but the ones that did exist like data classification tools created a lot of false positives and had scaling issues. “There wasn’t a tool that could help you go from detection to actual recovery, which is processing the data, isolating the data, or taking any kind of action on the data.” The solution Telescope has built enables customers to connect to their various data sources, locate sensitive data in a variety of data warehouses in an automated manner, and isolate or delete it when needed.
Right now they have a few different use cases: “Primarily now we’re selling to data teams, not just product developers, but data management engineers, who want to clean entire datasets in their data warehouse, or want to clean a dataset before it use to train models or have multiple data sets and need to delete data for a particular user for compliance reasons,” he said.
The solution is based on what Trias calls “a pipeline of models” with different ones coming into play depending on the type of data. “So, for example, we’ve trained a model that is very good at classifying natural language data, such as chat file types. We have trained a model that works very well with structured array types. We have trained a model that can classify sensitive data in a code base file or a log file,” he said.
Trias says that while they had the background and pedigree to build a product like this, they didn’t know much about the world of venture capital and how to pitch when they first started the company — and female founding teams face a bigger challenge with investors in general. “I think the hardest part was that when we first started making VC calls, we had no idea how to do it. We didn’t even know what a design partner was. We were pre-product, before anything, and we didn’t know all the VC lingo. And so we were very unprepared when we first had our first meetings with the VCs,” he said.
Over time they refined their pitch and were able to find investors who bought into them and their vision. Seed funding came from Primary Venture Partners with participation from Lerer Hippeau and Plug and Play Ventures along with Essence VC, which led the company’s pre-seed round.
