Each model began as a question we couldn’t answer with conventional methods. ArcOne reasons across disciplines. Oracle perceives what instruments miss. Object moves through spaces no map can describe. They share an architecture because intelligence isn’t three things — it’s one thing, expressed differently.
These models exist for the decisions that can’t be wrong. Each is purpose-built for a class of problem where the cost of error is measured in lives, in billions, or in years that can’t be recovered.
A pharmaceutical team needed novel antibiotic candidates against carbapenem-resistant bacteria. ArcOne cross-referenced protein dynamics, evolutionary pressure, and soil microbiome data simultaneously — identifying 4 compound families in 6 weeks. Traditional screening would have taken 2+ years.
A sovereign wealth fund evaluated 340 opportunities against a 200-page mandate, full market data, and geopolitical risk models. Oracle processed every data room and returned binary invest/pass verdicts. Portfolio performance improved 18% year-over-year in the first deployment. One verdict — pass on a late-stage deal — saved $1.2B when the company filed for bankruptcy 11 months later.
In a collapsed-structure exercise, Object was deployed with zero prior mapping data. It built a spatial model of 3 floors in real time, identified 12 simulated survivors in 40 minutes, and produced a structural assessment that matched a 4-person engineering team’s report at 96% agreement. No human entry was required.
General models optimize for breadth. Ours optimize for the weight of the decision. Each model is trained on domain-specific corpora, evaluated against domain-specific benchmarks we built ourselves, and deployed with domain-specific safety constraints. The result is a tool that doesn’t just perform — it performs where performance is non-negotiable.
Intelligence isn’t one thing. Reasoning across papers is a different cognitive act than perceiving a pattern in 4 million data points, which is different from building a spatial model of a room by touching its walls. One architecture. Three expressions. Each tuned to the physics of its problem domain.
Industry benchmarks measure the wrong things. SWE-bench tells you how well a model writes code. It tells you nothing about whether it should approve a $400M investment or flag a drug interaction across 14,000 pages of clinical trial data. We built our own metrics because the questions we’re answering didn’t have scores yet.
Every claim on this page traces back to a published result. We don’t announce capabilities we can’t demonstrate, and we don’t demonstrate capabilities we can’t explain.
Our models are available to qualified institutions through a structured evaluation process. Tell us what you’re building and we’ll determine if there’s a fit.