The exponential rise in “knowledge” and the increasingly distributed nature of work have increased the time needed to find existing knowledge. In other words, “searching for stuff” at work is fairly broken.

As organizations become more distributed and knowledge becomes more fragmented, an intuitive work assistant like Glean is no longer a nice-to-have but a critical tool in driving employee productivity.

Today, one of the key obstacles preventing enterprises from shipping AI applications to production is their inability to enforce appropriate governance controls (e.g., “Does my application understand what the end user is allowed to see and not see?”; “Is the inference done on my servers or OpenAI’s servers?”; “What source data led to a given model output and who owns it?”).

Data processing and annotation remain the most tedious and expensive part of the AI process but also the most important for quality outcomes. Even with the rise in pre-trained large language models, enterprises need to focus on using their proprietary data (across multiple modalities) to create production AI that leads to differentiated services, insights, and increased operational efficiencies.

Traditionally, humans take days to complete a straightforward but time-consuming task like classifying e-commerce listings with multiple paragraphs of text. With GPT-4, however, that task can be performed within hours.