Problem Statement

In the rapidly evolving artificial intelligence sector, companies encounter substantial challenges in data acquisition, management, and utilization due to traditional centralized systems. AI and data have been of rapidly growing interest to the tech space (see here, here, here for example), with many startups raising at very lofty valuations. In a typical enterprise, there are many disparate datastores across which the company's proprietary data is stored. This includes both internal systems (e.g., homegrown SQL databases, Excel spreadsheets, shared drives), as well as third-party systems (e.g., HR, vendor management, CRM). Moreover, each datastore has its own data format and unique data update process and cadence.

Current systems lead to inaccessible, siloed, and vulnerable data ecosystems, significantly hindering the ability for AI models to be effectively trainer, fine-tuned, and scaled. The centralized and disparate nature of data repositories not only limits access to diverse, high-quality datasets essential for training sophisticated AI models but also raises concerns about data monopolization, privacy, security, and ownership. This centralization stifles collaboration, contributes to potential biases in AI systems, and lacks the transparency needed to maintain public trust and regulatory compliance.

The integrity and usability of data present additional obstacles. AI companies frequently deal with datasets of poor quality, such as inaccuracies, inconsistencies, and relevancy issues, which can propagate errors and biases within AI systems. The extensive need for data preparation, including parsing, cleaning, labeling, and structuring, adds another layer of complexity. This labor-intensive process not only prolongs development cycles and escalates costs but also poses a risk of introducing further biases and errors during data handling.

Solving these problems can be achieved through a modern, decentralized data platform that not only democratizes data access but also ensures data quality and simplifies the data preparation and management process.

LLM Protocol was born tackle these problems in a holistic way, leveraging our team’s deep experience in building and scaling AI systems. Read on to learn more about what we are doing and join us in revolutionizing and democratizing the backbone of every single AI platform - data.