Hello everyone! We just finished the first version of our solution DataPond.AI - a platform for securing and tracking data used by AI. Please, see the highlight summarised below. I am available to answer questions and explore synergies with ICP community members. Any feedback is welcome!
Project highlights
DataPond is a hub for exposing and tracing data used by AI to generate results. It enables end users and businesses to store files on Internet Computer and to control how they are consumed by AI tools as input for training, reasoning, or producing answers.
DataPond is available as an open-source toolbox for developers who want to bridge their decentralized applications with AI transparently and efficiently. On the other hand, the solution will emerge as an end-user platform helping digital creators, data providers, and businesses use AI as a new distribution channel.
Web3 advantages
The key advantage of DataPond is the ultimate level of transparency and traceability regarding how AI consumes data for generating results. Each interaction with the files stored on-chain on ICP generates a trail that serves as a reliable accounting of data consumption.
Internet Computer superpowers
Internet Computer currently is the only protocol that enables fully decentralized applications with on-chain data storage. Our canisters support the upload and storing of data on ICP which guarantees the independent functioning of DataPond’s platform without any limitations and dependencies on centralized cloud infrastructure.
Go-to-market strategy and monetization
DataPond will be available as a free and open-source building block to boost trust and transparency in AI data usage and consumption. Our strategy is to offer DataPond as a freemium online service for uploading documents and creating chatbots connected with the provided data. In that way, we will open up a funnel with prospects and leads.
DataPond’s architecture is perfect for creating collections of thematic data sets (documents, images, database records, etc.) that feed AI models to produce quality results. Our strategy is to foster the adoption of the solution by focusing on specific market segments and enhancing the performance of the chatbots via contextual augmentation, adding context and metadata, curating with questions and answers, etc. This will provide a competitive advantage and will attract a user base.
The regulatory landscape will further create a demand for solutions such as DataPond since explainable and transparent AI requires evidence-based references and efficient verification of data used by chatbots and agents to generate the results.
Full information regarding DataPond’s implementation is available here:
Demo of the user experience of DataPond: https://www.youtube.com/watch?v=sgNbZKwVCog