Manufacturing
Over the past decade, Brian Raymond and the founding team of engineers at Unstructured Technologies have been navigating a consistent challenge in the natural language processing (NLP) space: clients eager to dive into AI/ML solutions, but hindered by the frustrating reality that their data was trapped in unusable formats. This common pain point became the driving force behind the creation of Unstructured, a company focused on solving the data preprocessing problem that has plagued the industry for years.
Data Bottlenecks in NLP
The world of data science has long been held back by a persistent challenge in NLP: preparing unstructured data for machine learning models. Data scientists were continuously forced to build bespoke, one-off data connectors and preprocessing pipelines to make natural language data compatible with AI algorithms. But these solutions were not scalable. Data scientists dreaded these bottlenecks, and with good reason: there was, and still is, a lack of tooling to effectively automate this crucial step. Without efficient ways to convert raw, unstructured data into usable formats for machine learning, the full potential of AI and large language models (LLMs) could never be realized.
The team at Unstructured recognized early that to unlock the true capabilities of LLMs, the core problem had to be solved: the ability to seamlessly connect, transform, and stage any form of unstructured natural language data at speed and at scale. This led to the development of Unstructured Technologies.
