DATA’s New Strategy: Transforming AI Data Transactions with Trace

DATA is evolving its business model as it seeks to address challenges in the AI data marketplace. While the company has not publicized the remaining amount from its initial funding of $140 million, it continues to maintain relationships with its current investors, including significant backing from a16z crypto.

Building on Previous Foundations

Rather than signaling another shift in direction, CEO Muttoni emphasizes that the latest strategy is a continuation of the first rebrand. This new focus draws from the company’s experience with Poseidon, an AI data project developed by the Story team. Poseidon specializes in the collection and processing of datasets for training AI models.

Identifying Market Gaps in AI Data

Muttoni highlighted a critical issue encountered during Poseidon’s operations: many buyers lacked reliable methods to verify the authenticity and licensing of the datasets they were acquiring. For instance, it was not uncommon for vendors to provide datasets labeled as exclusive that were readily available from alternative sources.

In one unusual incident, the team even received back a dataset that they had created themselves.

Introducing Trace: A Solution for Transparency

In response to these market challenges, DATA is launching a new product called Trace. This tool is designed to enhance transparency and integrity in data transactions.

With Trace, when a user uploads a video or audio file to a participating data marketplace, the system generates a receipt containing essential information. This includes:

• The submission date of the file
• The terms agreed upon by the contributor
• Any subsequent licensing or payment actions

Marketplace Dynamics: The Role of Kled

A significant portion of the activities on Trace is driven by Kled, a marketplace incentivizing users to contribute data. According to Muttoni, Kled is currently experiencing substantial growth, recording between 3 million and 4 million contributions per day. This figure includes older datasets being integrated into the Trace system.

Conclusion

Through the introduction of Trace and its commitment to improving data integrity, DATA is positioning itself strategically within the evolving landscape of AI. By addressing the prevalent issues of dataset verification and licensing, the company aims to foster trust and reliability in AI data transactions.