Domain-specific Analytics Enhance and Scale Human Expertise
Data may be the new "natural resource" as our modern economy shifts to make use of even more insight in critical processes; but it can easily become too abundant for decision-makers to quickly translate into practical actions.
Well-designed analytics can help us look at the world through the lens of "big data," with the same kind of automated reasoning behind popular voice-, text- and image-recognition apps that can efficiently spot new patterns and anomalies in raw bits of unstructured data.
We've complied comprehensive sets of these capabilities into domain-specific "A.I. engines" frameworks deeply rooted in machine learning that target well-defined processes in healthcare, education, and manufacturing; where context is important for arriving at the right answers.
These creations approximate the multi-layered logic that interconnected webs of neurons in the brain use to ingest and then crunch through vast amounts of information improving with experience at all sorts of useful tasks and, in many cases, outperforming human experts by learning to do a task faster and at much greater scale.
And we also purposefully invest in strategic partners to unlock even more benefit from these tools by extending their utility to solve a wider range of practical problems ultimately, to perform professional-level tasks including projects that can fine-tune training parameters to the unique context of each targeted industry sector; and expert supervisors who can recognize systemic risks, make sense of anomalies, and analyze tradeoffs to form better decisions under uncertainty.