Ray: For sure, my name is Ray Gill. I'm the founder and CEO of DataHive. At DataHive, we believe that the future of AI is all about users running it locally on their devices, powered by user-owned data. One of the biggest missing pieces is that users don't own their data—it currently resides with external enterprises, and there is no scalable way for users to take ownership of their information. That's why we're building the world's first privacy AI agent to help users and enterprises transform data liabilities into data assets, which forms the foundation of the decentralized AI we're working toward.
Ray: Yeah, I've always challenged myself, especially from a young age. I always put myself in positions and roles where quick, steep learning is involved. I enjoy learning, and I think I'm able to pick up things fast.
At DataHive, I advised them that they were in the email marketing space, helped them with their expansion, and helped them have an IPO. As part of that engagement, I interacted with a lot of retailers and enterprises who were seeking to build consented data relationships with users. I also saw that this big shift around privacy, at that time, was just starting. So, I started connecting those two dots, which became the foundation for DataHive. This required a lot of testing, validating different ideas and concepts, and speaking with customers. But all of that has been super helpful in becoming the foundation of DataHive.
Ray: Data, from our perspective, has become a liability and needs to be an asset. And it's a liability for both enterprises and users. For enterprises, it's a liability because their employees can now be targeted for manipulation and other issues from the data these AI models can collect. As you know, AI is able to clone a voice and analyze data rapidly. So, as data brokers collect data, it’s profiled and sold to unknown companies. Now, any of those companies can buy data on individuals and use it to target them with manipulation and other informational harms.
On the user side, it's a similar story in that our data has monetary value. We should have ownership of it and be able to choose who to share it with and what the transaction entails.
As I mentioned, this data asset is valuable for both enterprises and users. That's where our AI data asset layer 2 comes into play. This data asset can be deployed and monetized to help enterprises power their data-driven use cases and, at the same time, help users run and control their own AI, which they run locally on their devices powered by their data assets.
Ray: The 0G team is great. They've been super supportive, and we've been building on the 0G platform from day one. We're using them as both a DA and a storage layer. We're storing data from our partners and customers in their storage layer, and as we launch our consumer app, many inference-related aspects will also be stored on 0G. All of this is collectively helping us move toward a decentralized AI powered by user-owned data. We share a very common goal and mission with the 0G team, and we're very much looking forward to building and growing this relationship with them.
Ray: We were looking for a system with high throughput—given the volumes we're dealing with—that allows us to analyze data rapidly since we're serving both enterprise and consumer use cases. Their speed was a big factor for us, and security was also crucial. We deal with many enterprise clients requiring SOC 1 and SOC 2 compliance, so security is paramount. Those two features were core aspects of their platform that fit well with how we build our ecosystem.
Ray: Unique advantages that DataHive has gained by utilizing 0G compared to traditional solutions can be broken down into, I'd say, four or five categories:
Ray: So, I think our growth was driven by the feedback and support from the OnPiece Labs team and the 0G team. We focused on securing key relationships and partnerships while really zeroing in on solving the core problem—helping users take ownership of their information. We're also partnering with academic institutions across the US, which has allowed us to grow quickly. We're launching in California and then scaling nationwide. We're super excited, and I think we have a lot more to share over the next few weeks.
Ray: With New York University's Stern School of Business, which has a division called Endless Frontier Labs, selected DataHive because they liked our mission of using science and technology to solve key issues worldwide. For us, our focus is very much on data privacy and data ownership. With NYU, we're collaborating on two fronts. One is a beta launch across our campus, where we're rolling out our AI agent to students. The second is a research project focused on how AI agents can protect employees, individuals, and even children from information harm and how we, as a society, need to prioritize data ownership. We'll have a lot more to share as that study is completed early next year, and that research will definitely play a big role in how we build and scale DataHive.
Ray: So, I think there are two main fronts. First, the incubator really helped us hone and refine our value proposition and improve how we pitch and present what we're building. Our solution has many moving pieces—we add value for both enterprises and consumers, and we needed to communicate that quickly to different audiences, whether it's investors, IT enterprises, or schools. Second, the support and partnership with 0G enabled us to quickly build and launch our beta. These two areas have been immensely valuable for us at DataHive.
Ray: I think it was the various feedbacks, like, even though the program was entirely online, I think after we finished pitching across the various meetings, there was also, or every time, there was really good feedback from the mentors and other folks on the call on how to become better. I think that was key. And also, I didn't shy away from calling Dafu and meeting in person. I've enjoyed meeting him even though the program was online. Hey, I made the time to meet Dafu and others and I've really enjoyed that experience.
Ray: We've got a note sale coming up. So we'll start sharing that on our socials very soon as well we'll be planning for TGE early next year. We've got a beta launch happening in Southern California across the various schools. So we're going to have a lot of users on our platform, helping us scale and refine our core value proposition. And there are some key enterprise partnerships we're just finalizing, which will help us accelerate our rollout even faster. So distribution plays a very key role. We want to be able to roll out the platform to as many enterprises and to as many users as we can, as quickly as we can. So we've been kind of building up for this. So we'll have a lot more to share over the next, next few weeks.
Ray: On the AI side—especially in the context of Web3—we need to figure out real use cases that add value to users, which is the biggest missing piece. AI can do a lot of things, and we're seeing companies like OpenAI and Google build use cases that are very work-related—for example, coding, writing, and even graphics. But from a user perspective, the challenge is determining how AI adds value on a daily basis. There's a huge opportunity here where data ownership sovereignty with AI needs to play a key role, which is perfectly suited for what Web3 enables as an industry. We believe companies will have a massive opportunity to build personalized user experiences. We're excited to be solving one piece of that: helping users take ownership of their information so that their data runs their AI, which they operate locally on their devices. That is going to unlock a huge set of capabilities in agentic commerce. We're very fortunate to be addressing this aspect, but there's still so much more to do.
Ray: I would say first figure out what it is that you want to achieve. Are you trying to get customers? Are you trying to raise capital? Are you trying to perfect your pitch? Or are you trying to do something else? Ensure you clearly understand where you want to be at the end of the program. Also, think about how you will leverage the program because it's only as good as how effectively you connect with and leverage the network within the incubator. Make sure you're active, know where you want to be at the end of the program, and be willing to take feedback—good or bad. I think every meeting and every touchpoint helps entrepreneurs think through and refine their business models. Don't be shy about taking criticism or receiving negative feedback; everything is just helping you become better. Just keep pushing forward, and don't take no for an answer. Keep pushing ahead.
OnePiece Labs is a premier Web3 accelerator dedicated to supporting mid-stage startups. Through mentorship, networking opportunities, and tailored programs, OnePiece Labs accelerates the growth of promising Web3 projects, shaping the future of decentralized technology.
Website: https://www.onepiecelabs.xyz
Telegram: https://t.me/onepiecelabs
Twitter: https://twitter.com/OnePieceLabs
Newsletter: https://substack.com/@oplabs
