Capabilities
Three problems decide whether an AI system can be deployed; whether its behavior can be measured, whether it runs where the mission is, and whether the model underneath can be trusted. We work on all three.
AI test and evaluation
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Test harnesses and evaluation infrastructure built on statistical rigor
An AI system that performs well in a demonstration tells a program office very little. LLM-powered systems are non-deterministic: run the same test twice and the score moves. Decisions cannot rest on a promising happy path execution and a handful of examples.
We structure evaluation for experimental repeatability to generate testable hypotheses and actionable statistically significant results. We size tests so it can capture and articulate a difference that matters while controlling conditions so runs are repeatable. At the conclusion we report effect sizes and confidence intervals alongside significance, so a program can confidently separate novel breakthroughs from noise.
Today’s testing environment requires disciplined approaches since many public benchmarks have been memorized or trained upon. This puts new premiums on freshly authored and unpublished material that cannot be scraped.
Frontier AI is built for cloud-connected data centers. The mission often happens far afield, on smaller airframes, ground vehicles, or handheld devices where power, weight, thermal budget and unit costs are constrained. Networking may be contested or absent.
We treat deployment platform constraints as design requirements from the start: choosing model sizes and quantizations that best fit the compute available.
AI at the edge
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Capability sized for low SWaP-C platforms
Trusted open-source models
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Performance from models with known provenance
Many missions cannot send data to a commercial API. Many programs are rightly wary of models whose weights, training data, and country of origin cannot affirm a trusted chain of custody or software BOM.
We build systems on models developed in the United States and run inside the customer’s infrastructure boundary. We believe the customer is the best custodian of their data.
Tell us what you need measured.
Where we are investing
Research directions we are actively developing.
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Techniques to keep model behavior predictable under adversarial pressure with testing harnesses keeping pace with the state of the industry.
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Trusted autonomy techniques applied to recognition and decision support for unmanned systems.
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Evaluation sets built from material outside public scrapes, along with synthetic and adversarially generated data for mission contexts outside benchmark domains.