2 years ago I was working as a software engineer at a large corporate. I was part of a team building a product for virtual classrooms and it inherently involved sensitive pupil data. I remember the day where I got the simplest request from our lead product: “I want to know the average grade of students in the classrooms to assess the impact of the system on exam performance”. Sounds like a legitimate 2-seconds analysis, right? wrong.
That’s when I first discovered the complexities of analyzing production data. An average exam grade could potentially be traced back to a specific student given additional information or queries. Statistics can either tell a broad story, or a very precise and targeted story, depending on the data, the questions and intent. Instead of extracting a simple average, I was only allowed to calculate approximate and very high-level figures.
It sounds cliché, but I swear I told myself “there has to be a better way”.
And there was.
During that time, Shachar was finalizing his Ph.D. in computer science, specializing in Differential Privacy. A mathematically quantifiable definition of privacy that was formulated precisely to unlock analytics without compromising individual privacy. We realized that this innovative approach has to be productized and available for all companies, and decided to found PVML.
Value Proposition and Raising Our First Round
As a solid and completely non-committal first step, we both quit our day jobs immediately. Being a married couple and putting our household income on hold to start a company was undeniably a risk, but we were fueled by classic founders’ optimism that clouded our typically numbers-driven judgment. We had a true confidence that, despite the infamous startup statistics, this could not fail. Plus, our all-consuming 24/7 obsession became our secret weapon; having been a couple for fourteen years turned out to be quite an unfair advantage, especially since work-life balance is a huge struggle for most startup founders.
Statistics turned out to be even worse that year; 2023 proved to be a particularly challenging year for Israeli high-tech, with a 50% drop in the number of companies raising a Seed round compared to 2022. Despite this, we successfully demonstrated that our solution—a new generation of data access platforms that eliminates the binary choice between “see raw data” or “see no data”—is precisely what the market was missing. This became especially impactful during the AI revolution, as AI increasingly accesses sensitive data, raising the stakes for data security.
The old-school data anonymization methods are officially inadequate, while companies are actively looking to integrate AI and speed up time to insight using free text. With PVML, we delivered exactly that: democratizing data access in a way that facilitates AI, backed by powerful Privacy-Enhancing Technology—Differential Privacy—to ensure data protection..
Data Peace Of Mind
PVML provides a secure foundation that allows you to push the boundaries.
To demonstrate the strength of our value proposition, we decided to begin our Seed fundraising by engaging with potential clients rather than investors. Our excitement grew with each conversation as companies described the challenges they faced with data access. We even managed to generate leads, which we later converted into proofs of concept and clients.
We successfully closed our $8M Seed round in the second half of 2023, led by the powerhouse VC NFX, with General Partner Gigi Levy-Weiss. We also earned the confidence and backing of Gefen, alongside Managing Partner Limor Ganot, and FJ Labs, with Partner Jeff Weinstein. Additionally, we were supported by key figures in the data security industry, including Ran Nahmias, Amit On, Erez Yarkoni, and Jonathan Lebowitsch. Their support was instrumental in the inception of PVML, and we are forever grateful.
The Dream Team
We believe that people are the most critical factor in achieving any goal that demands collaboration, innovation, and courage. Our goal definitely required all three—and more—so we knew we needed nothing less than a superstar team of engineers and researchers by our side. We reached out to our most talented former colleagues and university friends, inviting them to join us on this crazy journey. The response was incredible—leading technological minds from giant corporations and fast-growing startups came on board. Each brought their unique talents and strengths, enabling us to assemble an exceptional team ready to drive groundbreaking accomplishments at PVML.
Introducing PVML’s Data Access Platform
To enable seamless integration, we allow users to connect any database using a simple connection string, making it a quick, 2-minute process with no infrastructure changes required. Admins gain a single, centralized point to monitor and control all data access, providing a high-level view of queries, permissions, user requests, and to-dos. Our system tracks excessive, unused, or conflicting permissions, helping reduce managerial overhead with one-click actions that enhance security and streamline operations.
We developed a SQL compiler capable of translating database queries into Differentially Private queries on the fly, without requiring users to alter their query syntax. This ensures privacy and permissions enforcement regardless of how users interact with the data—whether through SQL, AI, or external BI tools. PVML takes charge of live, output-level data protection, delivering seamless and secure access to sensitive information.
