On September 19-21, 2023, the Prifina team attended the largest frontier tech event of the year in the Bay Area - TechCrunch Disrupt 2023. We had an opportunity to meet new and current partners and showcase some of the use-cases where Prifina’s partners are leveraging Prifina’s personal data platform to offer services on top of users’ own data. 

In the post below, we summarize some of our main accomplishments during TechCrunch 2023, and also provide some insights about the current trends in the consumer data and AI market.

Prifina's Hub at TechCrunch Expo Hall

This year at TechCrunch Disrupt, the Prifina had a booth located in the very center of the expo hall thus becoming a hub of attraction for people who came to see what are the latest developments in frontier tech.

At our booth the Prifina team primarily aimed to focus on two areas of our work: 

• Showcasing how user-generated data from sensors could unlock the opportunities for brands;
• Showcasing how Prifina's human-centric approach to data unlocks new opportunities to build Personal AI assistants and agents. 

Showcase of sensorized products 
​At Prifina's stand, visitors could see and experience how sensors are added to the physical products we use daily. Specifically, the Prifina team brought real sensorized helmets and headbands produced by our partners Haierling (making sensorized ski boots) and Samphire Neuroscience (producing menstrual neuromodulation devices). 

Those devices helped us illustrate how Prifina empowers individuals to collect data from various sensors and platforms and build compelling solutions on top of consolidated users' data. In the case of a ski coach, for example, the user not only gets a report about the ski performance but can also talk to a personal AI ski coach about the recovery after an intense ski experience. Such recovery is possible because the user can collect data from various data sources (e.g., ski boot sensor and a sleep tracking device such as Apple Watch or Oura Ring).

Showcase of Personal AI Applications

As we enter a world where every service we use is transforming into AI-powered assistants, one question remains: how do we make AI assistants really personal to the user?

With Prifina's human-centric data infrastructure, it is rather simple to build AI-powered assistants: we need to build the assistant to the user, and tap into the combined of the user. 

Here is how it looks in practice: assume you want to build a "Personal AI travel agent" app. What data would such an AI assistant need to make recommendations that are truly personal to me? In this case, perhaps it is about: 

(i) my previous bookings on AirBnb platform, 
(ii) my starred locations on Google Maps (places I liked and places I marked as to be visited in the future); (iii) obviously, my calendar(s) so that I could plan accordingly, and 
(iv) perhaps my payments history.

In Prifina's architecture, the user downloads the "Travel AI Assistant App" and runs on top of his/her own data, which includes various data sources, also possible is the four mentioned above. Such Travel AI Assistant app runs privately (on top of device, or in my own personal data hub which only I can access). 

Prifina's human-centric data architecture received much attention; we are onboarding new partners to build applications that run privately on the combined data set that users have in their personal data environments.
​
General Takeaways and Market Trends 

After spending three days and talking to hundreds of founders, partners and investors, we have the following three takeaways about the market trends: 

1. AI everywhere, even where it is not needed
There is so much buzz about Artificial Intelligence. There is no question that AI tools and AI solutions are coming to many areas of our work, social life and leisure. AI tools will help curtail the amount of redundant tasks and help people focus on things that matter more, increase efficiency. In fact, this process of AI tools has been already taking place for quite some time, but the emergence of OpenAI's tools such as ChatGPT has made it really obvious.

We also observed that the AI hype could get out of control: namely, we see how startup founders and builders are trying to add "AI" even to those areas where it does not make any sense. The result? Clumsy and complex UX, forced functionalities that are not needed. We understand that is a natural process of trial-and-error; we just hope that it will not take too much time and resources. 

2. Sensors and Empowerment with Raw Data 

Similarly to other technology conferences in 2023, we see witnessed the wave of sensors  rapidly entering every aspect of your physical lives. From sensorized running shoes, sensitized toilet seats, complex sensorized exoskeletons to sensorized workspaces, sensors and transforming how we  live, work and recover. 

3. Predictive Analytics for Consumers

At TechCrunch we evidenced many fertility, sexual, reproductive, and other solutions that rely on sensor data and provide insights and nudges for consumers. Some of those solutions are diagnostic and clinical focusing on sophisticated biometric data, others are simple consumer-facing apps.

Yes, one of the key common thread among sensors and AI-powered apps that offer predictive analytics is the need to adopt a more holistic approach to data. That's where Prifina's human-centric data model for user generated data has received most attention and validation.

We are looking forward to continue our work with Prifina's current and new partners to unlock the value from users' data created across platforms and devices. Showcase of sensorized products 


At Prifina's stand, visitors could see and experience how sensors are added to the physical products we use daily. Specifically, the Prifina team brought real sensorized helmets and headbands produced by our partners Haierling (making sensorized ski boots) and Samphire Neuroscience (producing menstrual neuromodulation devices). 


Those devices helped us illustrate how Prifina empowers individuals to collect data from various sensors and platforms and build compelling solutions on top of consolidated users' data. In the case of a ski coach, for example, the user not only gets a report about the ski performance but can also talk to a personal AI ski coach about the recovery after an intense ski experience. Such recovery is possible because the user can collect data from various data sources (e.g., ski boot sensor and a sleep tracking device such as Apple Watch or Oura Ring).


Showcase of Personal AI Applications
  
As we enter the world where every service we use is transforming into AI-powered assistants, one question remains: how do we make AI assistants really personal to the user?


With Prifina's human-centric data infrastructure, it is rather simple to build AI-powered assistants: we need to build the assistant to the user, and tap into the combined of the user. 


Here is how it looks in practice: assume you want to build a "Personal AI travel agent" app. What data would such an AI assistant need to make recommendations that are truly personal to me? In this case, perhaps it is about: 
(i) my previous bookings on AirBnb platform, 
(ii) my starred locations on Google Maps (places I liked and places I marked as to be visited in the future); (iii) obviously, my calendar(s) so that I could plan accordingly, and 
(iv) perhaps my payments history.


In Prifina's architecture, the user downloads the "Travel AI Assistant App" and runs on top of his/her own data, which includes various data sources, also possible is the four mentioned above. Such Travel AI Assistant app runs privately (on top of device, or in my own personal data hub which only I can access). 


Prifina's human-centric data architecture received much attention; we are onboarding new partners to build applications that run privately on the combined data set that users have in their personal data environments.