7 Go-to-Market Lessons from Private AI’s Journey in Privacy Engineering

Sometimes the most valuable market opportunities emerge when regulation outpaces technology. While many viewed GDPR as a burden, Private AI saw it as an invitation to innovate. In a recent episode of Category Visionaries, founder Patricia Thaine revealed crucial go-to-market lessons from building a privacy engineering company in an emerging category.

1. Know When to Kill Your Darlings

The path to product-market fit often requires abandoning initially promising ideas. Patricia shares how Private AI’s first iteration in 2017 focused on “combining homomorphic encryption with natural language processing to do semantic search on encrypted data.” Despite interest from financial institutions, they ultimately scrapped this approach because “it wasn’t going to scale.” This willingness to abandon a technically interesting but commercially challenging solution proved crucial to their later success.

2. Let Your First Customer Guide Your GTM Strategy

Finding your first customer can provide unexpected insights into your go-to-market approach. “That was by pure chance because somebody I was talking to had talked to a friend who had to solve this problem quickly or their customer would be unhappy,” Patricia recalls. This serendipitous connection didn’t just provide revenue – it helped shape their entire sales approach. The key lesson? Your first customer can teach you how to sell your product more effectively than any amount of theoretical planning.

3. Build Your Category Through Customer Language

When creating a new market category, speaking your customers’ language is crucial. Private AI’s approach involved “testing different types of messaging, keeping track of which messaging works and which doesn’t, looking at how people are talking about it out there.” They actively sought customer input, asking prospects “What terminology do you use when you talk about this problem?”

4. Use Market-Specific Go-to-Market Strategies

Different markets require different approaches. While Private AI started in North America, they discovered unique opportunities in Europe where, as Patricia notes, “they’ve been through a history that has made them appreciate privacy in a way that we have not fully grasped yet here in the US and Canada.” This insight led them to adapt their go-to-market strategy for different regions, resulting in their “approximately four X last year” growth.

5. Design Your Architecture for Your Target Market

Technical architecture decisions can become key differentiators in your go-to-market strategy. “We believe in making sure that data gets transferred to as few parties as possible, and therefore we deploy directly in our customers environment,” Patricia explains. This architectural choice wasn’t just a technical decision – it became a crucial selling point for privacy-conscious enterprises.

6. Build for Global Scale from Day One

While many startups focus on their home market first, Private AI made an early decision to build for global scale. “We strongly believe that privacy isn’t just for the English speaking world,” Patricia emphasizes, explaining their support for 47 different languages. This commitment to global accessibility has helped them secure adoption “in North America as well as Europe and Asia Pacific.”

7. Position Your Solution Within Larger Industry Trends

Private AI’s success partly stems from their ability to position their solution within broader industry developments. As Patricia notes, “Tech is now playing massive catch up to be able to help organizations comply with those data protection regulations.” By framing their solution as part of this larger technological evolution, they’ve made their value proposition more compelling to enterprise customers.

The journey of Private AI demonstrates how technical founders can successfully navigate emerging markets by combining deep technical expertise with strategic market understanding. Their experience shows that success often comes not from being first to market, but from being first to make complex technology accessible and valuable to enterprise customers.

For founders building in emerging technical categories, these lessons highlight the importance of balancing technical innovation with market reality. As Patricia puts it, “Privacy engineering tools are really nascent,” suggesting that the greatest opportunities often lie not in building the most advanced technology, but in making that technology accessible and valuable to the market.