Machine learning model development needs to be methodically, with extreme care, in order to yield effective results as unintended consequences can emerge at the various stages of model development. Flexible data ingestion policies allow companies to simplify ML model development by reducing the time needed for the model to teach itself and achieve greater levels of accuracies. However, enterprises are often unable to collaborate on model development as a result of the risk associated with data security in a model developed by multiple stakeholders. Cape Privacy is an encrypted learning platform that allows companies to collaborate on machine learning models without the risk of compromising any proprietary or sensitive data. The data remains encrypted throughout and can be plugged into models seamlessly, allowing data scientists to truly harness the power of machine learning with efficiency and unprecedented speed.
AlleyWatch caught up with CEO Che Wijesinghe to learn more about the inspiration for the business, future plans, latest round of funding, which brings the total funding raised to $25M, and much, much more.
Who were your investors and how much did you raise?
Cape Privacy recently announced its Series A of $20M.
The Series A round was led by Evolution Equity Partners with participation from new investors Tiger Global Management, Ridgeline Partners, and Tom Noonan of Downing Lane, together with existing investors Boldstart Ventures, Version One Ventures, Haystack, Radical Ventures, and Jevon MacDonald. Additional investment came from Fred Ehrsam, Ben Porterfield, Keenan Rice, and Partners of Sand Hill East.
Tell us about the product or service that Cape Privacy offers.
Cape Privacy is the leading global encrypted learning platform. It allows companies to collaborate on machine learning models without compromising proprietary or confidential data. Privacy is protected by default on Cape’s platform as companies share data with external parties to enrich data models and increase business value.
What inspired the start of Cape Privacy?
We founded Cape because we want to live in a world where we can have the benefits of technology without compromising privacy. Today, data science and privacy are at odds. Machine learning can truly help us solve some of the most important problems, but the data required is often sensitive, personal, or proprietary. Encrypted learning will accelerate the adoption of AI responsibly, in the most critical applications across industries.
How is Cape Privacy different?
On the spectrum of privacy-enhancing technologies, encrypted learning —Cape’s offering —has several advantages: it’s more secure than anonymization, it doesn’t affect utility compared to differential privacy, and it is more flexible than federated learning.
What market does Cape Privacy target and how big is it?
The current target market is focused on the global financial services sector, which is expected to grow from $20.49T in 2020 to $22.5T in 2021 at a compound annual growth rate (CAGR) of 9.9%. Beyond Financial Services, Cape is already seeing interest from Life Sciences where the total spending on medicine is $428B and the US Government, the single largest enterprise in the country, which has budgeted $92.17B in 2021 for federal IT spending.
What’s your business model?
Cape’s Cloud brings multiple parties together to work on protected datasets for machine learning and data science while maintaining privacy by default. Companies subscribe to Cape’s SaaS platform based on an ARR license agreement. We have a usage-based pricing model that is measured by the amount of data that is being processed by Cape.
How has COVID-19 impacted the business?
From the Company’s inception, Cape’s team has always been fully distributed across the United States, Canada, and Europe, working productively across 10 time zones. While many firms struggled to reorganize at the start of the pandemic, Cape was able to continue its business operations without any negative impact.
What was the funding process like?
Cape was not actively fundraising, but we were approached by a number of VCs who were interested in what we were doing. When we were introduced to Richard Seewald at Evolution Equity Partners, there was a great fit both in terms of persona, but also the depth of experience in our technical space. Once we signed Evolution as our lead, finding follow-on investors was quite streamlined. John Curtius from Tiger Global Management immediately saw the market potential and joined the round with a seven-figure investment. Our existing investors were all extremely bullish about Cape’s progress and all anteed up for their pro-rata allocations. Finally, we were very excited to add a number of strategic investors to the round including Ridgeline Partners, who focus on the US government and Tom Noonan, who is a well-known investor and serial entrepreneur in the data security space.
What are the biggest challenges that you faced while raising capital?
Timing was everything for the fundraising event. We had multiple investors jockeying to lead the Series A, so we had to delicately let down a number of parties once we had a term sheet in hand. That’s probably the biggest challenge we faced – we needed to ensure we had the terms of the financing agreed to in writing, while you never want to give other people bad news.
Timing was everything for the fundraising event. We had multiple investors jockeying to lead the Series A, so we had to delicately let down a number of parties once we had a term sheet in hand. That’s probably the biggest challenge we faced – we needed to ensure we had the terms of the financing agreed to in writing, while you never want to give other people bad news.
What factors about your business led your investors to write the check?
The market potential is huge. Our investors believe that Cape has the ability to transcend industries, to become a defacto standard for secure machine learning and data science. The deep technical subject matter expertise in cryptography and machine learning was a clear differentiator, together with a solid go-to-market strategy.
What are the milestones you plan to achieve in the next six months?
Focus on execution is paramount. We will be doubling our engineering team, as well as adding strategic hires across other departments. Ensuring customer satisfaction is key and we are already investing in a head of customer success to ensure that process and function scales as we add more users to the Cape platform.
What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?
Be frugal with your cash. Only hire essential team members. Focus on product-market fit as the priority. Don’t burn cash getting stuck in extended unpaid lab experiments. If the business pain is big enough, there should be budget to trial your product. Perhaps this is an obvious observation, but raising capital with customer traction and revenue is much easier.
Where do you see the company going now over the near term?
Building on our success in the Financial Services industry, we have already had great interest from Health and Life Sciences companies for potential drug discovery and genomics research use cases. In addition, there is clear demand for this technology for collaboration on machine learning model development across Government agencies for counter-terrorism programs.
What’s your favorite outdoor dining restaurant in NYC
Wayan in Nolita…fantastic Indonesian cuisine with a modern French flair.
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Source: https://www.alleywatch.com/2021/05/cape-privacy-encrypted-learning-machine-learning-models-collaboration-che-wijesinghe/
