More than half or more of data generated in organizations is considered dark i.e. information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).

As AI bottlenecks of compute are becoming less central, we need to shift our focus on unlocking this data & create AI/ML models and agents i.e. AI assets using it. This data lives mainly off-chain today and is critical to build domain specific models and agents. At Reppo, we coordinate AI infrastructure and incentivize AI modelers and developers to bring utility to it for companies and blockchain networks. Furthermore, the derived IP is co-owned and co-monetized.

After spending many months next to developers and builders, we have learnt that developers care less and less about grants and prize money at hackathons and more about benefits to them as the primary creators of the IP assets i.e. models, agents as well as the intermediary blocks to productionize AI.

In the world Reppo is enabling, ownership, monetization, and collaboration are central to empowering developers who have been disenfranchised in the AI revolution. We are spearheading a new economic paradigm that enables fractional ownership and co-monetization of AI assets.

By coordinating existing Decentralized AI hardware and incentivizing developers to become owners and unlock value from dormant data, we are building a permissionless network that liberates intelligence from centralized walled gardens.

The Technical Problem: Unused Data in Centralized Silos

Limited access to training data is stalling the development of advanced AI models and agents and the impact is significant. Without diverse, high-quality datasets, independent developers and small organizations struggle to build and refine models that solve real-world challenges while large tech companies leverage their proprietary data to develop state-of-the-art AI. This imbalance constrains the AI ecosystem, limiting innovation and equitable development.

Our Solution: Pool proprietary data, Coordinate existing resources, while incentivizing decentralized production of AI

Coordination Layer = Communication + Commitment Layer

No easy way to communication between Infra today

There’s a lot of available Infrastructure, both centralized and decentralized, but because it does not communicate with each other and is 2 to 3 layers abstracted away from the end-user, it is painful for developers & enterprises to sift through, piece together, and just use it to build useful stuff. Tokenomics is also confusing and irrelevant to developers and enterprises.

So many picks and shovels, it gives anxiety to the handyman

At Reppo, we are combining innovations in zkTLS, federated learning, and Anoma’s intent-centric architecture to bring utility to all existing infrastructure. We are also building a pricing engine that is designed after FTR auction mechanisms.

Commitment Layer non-existent

By commitment layer, we mean a way for data contributors and developers to commit to resources as well as their end-users, in the absence of a centralized entity.