Capability and... ...capability and...
Frontier AI models are remarkably capable, but they’re also probabilistic. The same input can produce different outputs, confidence varies, and behavior evolves as models improve.
Production software demands something different: consistency, predictability, and resilience.
Bridging that gap requires more than prompting. It requires systems that observe, validate, recover, measure, and continuously improve—making non-deterministic models dependable enough for real-world software.
That’s the class of engineering problems we’re interested in.
Our perspective.
The capability of frontier AI doesn’t end with the model itself. The systems built around it determine how consistently it performs, how effectively it collaborates, and how much of that capability is ultimately realized.
We’ve demonstrated this with Descant: a fully autonomous software engineering system that combines existing frontier models into a single engineering organization. In publicly released, reproducible evaluations, Descant significantly exceeds the software engineering performance of the underlying models running independently.
We think that’s an important pattern. Better systems don’t just make frontier AI more dependable—they can make it substantially more capable. As frontier models improve, those systems improve with them.
Our goal is to make those capabilities accessible to far more people.
Say hi.
We’re engineers with decades of production experience building systems people depend on.
We like software that’s boring in the best possible way: predictable, observable, maintainable, and resilient. The kind of infrastructure people stop thinking about because it quietly does its job every day.
If you’re building that future too, we’d love to talk.