Topics
Latest
AI
Amazon
Image Credits:Peresmeh / Getty Images
Apps
Biotech & Health
Climate
Image Credits:Peresmeh / Getty Images
Cloud Computing
Commerce
Crypto
enterprisingness
EVs
Fintech
fundraise
Gadgets
punt
Government & Policy
ironware
Layoffs
Media & Entertainment
Meta
Microsoft
Privacy
Robotics
security department
societal
Space
startup
TikTok
fare
Venture
More from TechCrunch
effect
Startup Battlefield
StrictlyVC
Podcasts
Videos
Partner Content
TechCrunch Brand Studio
Crunchboard
adjoin Us
When Julie Trias and Elizabeth Nammour were working together at Airbnb on the fellowship ’s datum squad , they had to take with data spread across a variety of sources , and that growing sprawl led to challenge in keeping datum secure . The founders ’ own foiling with the existing crop of information protection options propel them to launch a company and build the automate data shelter tool they want .
On Tuesday , that inauguration , Teleskope , announced a $ 5 million seed investiture .
“ We tested a bunch of different tools to serve us empathize , protect , delete and redact data point at Airbnb , but what we occur to recognize is that there was n’t that tool that could help developers do this automatically , ” Trias told TechCrunch .
That ’s not to say there were no solutions , but the one that existed like data assortment prick generate a lot of mistaken positives and had scale outcome . “ There was n’t a shaft that could help you go from detective work to existent remediation , which is redacting the data , isolating the data or taking any sort of action on the data . ” The solution Teleskope has built enables customers to connect to their various data source , identify sensible data point across a variety of datum stores in an automated way and isolate or erase it when necessary .
They currently have a few different usage cases : “ We ’re mainly now selling to information squad , not just a product developer , but data governing engineers , who want to strip up their intact datasets in their datum warehouse , or they want to cleanse one dataset before they use it for model training , or they have multiple datasets , and they require to cancel data for a particular exploiter for compliance purposes , ” she say .
The resolution relies on what Trias calls “ a pipeline of models ” with dissimilar ones coming into play , depending on the type of information . “ So for example , we ’ve trained a model that ’s really good at classifying data point in natural nomenclature like colloquial types of files . We ’ve trained a good example that work really well with structured tabular type of formats . We ’ve trained a model that can classify tender information in a code foot file or a log single file , ” she allege .
Trias say that in spitefulness of having the background and pedigree to build a product like this , they were n’t well poetize in the earth of venture capital and how to pitch when they first launched the companionship — and female origination teams facea bigger challengewith investors in general . “ I think the hardest part was that when we first take off construct VC calls , we had no approximation how to go about it . We did n’t even know what a design cooperator was . We were pre - product , pre - anything , and we did n’t bang all the VC lingo . And so we were very unprepared when we first remove our first meetings with VCs , ” she said .
Join us at TechCrunch Sessions: AI
Exhibit at TechCrunch Sessions: AI
They refined their intro over time , and were able to get hold investor who got them and their visual sensation . The semen funding was lead by Primary Venture Partners with participation from Lerer Hippeau , Plug and Play Ventures and Essence VC . Lerer Hippeau led the pre - seed round .
