Building in Nascent Markets: Private AI’s Approach to Category Creation

When GDPR first emerged, most organizations weren’t technologically ready to comply. Now, as companies scramble to protect unstructured data, the gap between regulatory requirements and technical capabilities is finally closing – creating the perfect conditions for category creation.

In a recent episode of Category Visionaries, Private AI founder Patricia Thaine shared how they’re building and defining the privacy engineering category, revealing crucial insights about timing, market education, and category development.

Identifying the Category Gap

“Privacy engineering tools are really nascent,” Patricia explains, positioning Private AI at the intersection of privacy engineering and data governance. Rather than trying to fit into existing categories like cybersecurity, they recognized an opportunity to define something new.

The timing proved crucial. “Tech is now playing massive catch up to be able to help organizations comply with those data protection regulations,” Patricia notes. This gap between regulatory requirements and technical capabilities created the perfect conditions for category creation.

Understanding Market Readiness

Their journey demonstrates the importance of timing in category creation. “It’s only very recently, in 2019, really, that machine learning started to be good enough to be usable for this problem,” Patricia shares. This technological maturity coincided with growing market awareness of privacy challenges.

The evidence of market readiness came from customer behavior. “Organizations, one of the main things that they had to do when GDPR came into play was to scramble to figure out what kind of data they actually had,” Patricia explains. This chaos signaled a market primed for solutions.

Educational Content Strategy

Rather than just selling features, Private AI focused on market education. They “post several blog posts about what’s going on in the privacy space, what kind of things to look out for in machine learning and privacy,” creating content that helps potential customers understand both the challenges and solutions.

This educational approach helped them identify diverse use cases. As Patricia notes, adoption comes from various needs: “Sometimes it’s compliance, PCI compliance, HIPAA compliance when it comes to the US. Sometimes it’s GDPR compliance, sometimes it’s data sharing, sometimes it’s risk analysis that they need to do in order to show the C suite.”

Global Category Development

Instead of focusing on a single market, Private AI took a global approach to category creation. “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 accessibility has driven adoption “in North America as well as Europe and Asia Pacific.”

Their global perspective revealed interesting market dynamics. In Europe, they found a unique appreciation for privacy solutions because, 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.”

Technical Decisions That Define the Category

Private AI’s approach to category creation extended to their technical architecture. “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 decision helped define what customers should expect from privacy engineering solutions.

Results and Future Vision

Their category creation efforts have yielded significant results, with the company achieving “approximately four X last year.” But perhaps more importantly, they’re shaping how organizations think about privacy engineering.

Looking ahead, Patricia envisions the category evolving toward edge computing: “Ultimately, I think in three to five years, hopefully we’ll be embedded directly within devices as well as people become a little bit more comfortable when it comes to privacy engineering.”

For founders building in nascent markets, Private AI’s experience offers valuable lessons about category creation:

1. Timing matters as much as technology
2. Market education is as important as product development
3. Technical architecture can help define category expectations
4. Global perspectives can reveal different levels of market readiness

Their journey shows that successful category creation isn’t just about having innovative technology – it’s about helping the market understand and embrace new possibilities while meeting them where they are today.