Decades in the domains where being wrong is not an option

When Bryant Cruse joined the Hubble Space Telescope Mission Operations team in the mid-1980s, operators determined spacecraft status by manually analyzing 4,000 constantly changing telemetry parameters. He had a vision of a better way. His first step was to build an expert system for on-board computer memory dumps. It cut analysis time from 45 minutes to under 2. This was the first ever application of AI to space operations.

His quest to automate space operations would span four decades and two previous startups, both acquired, leading finally to New Sapience. Sapiens are the final of three successively more advanced approaches to solve the core problem of automation: scale. Traditional expert systems and LLMs share the same failure: complexity grows exponentially while capability plateaus. The sapiens architecture inverts that. Capability scales while complexity stays bounded.

Today, systems are becoming more complex, critical infrastructure ages, and costs and the risk of cyber-attacks rise. Humans in the loop, making real-time decisions under stress with cognitive performance degrading precisely when the stakes are highest, are an added risk. They were a necessary one, because the operation of these systems is not possible without human expertise. But now that same expertise can be automated with cost-effective sapiens solutions.