Transformer-based large models were built for the cloud: huge memory, huge compute, huge retraining cycles. Cortex is built on a different substrate — and the difference is what makes on-device credibility assessment economically viable.
One Cortex instance on a single NVIDIA H100 GPU can serve 2,500 concurrent users at 250 tokens/s. GPT-5 manages 16. Claude Opus, 8. Same silicon, roughly 156× the concurrency. That ratio is what makes it possible to take the same model that runs in the cloud and run it on an edge AI accelerator inside the SatCam device — no compromise version, no distillation.
Traditional deep learning needs a 12–18 month training cycle, then validation, then deployment. Cortex learns in real time from every interaction. No retraining run. No catastrophic forgetting. The device you field in 2028 will outperform the one fielded in 2027 — because the model has seen more interviews, not because engineering shipped a new build.
Cortex separates reasoning from language. Cortex extracts symbolic concepts from the interview; it then operates on those symbols with an auditable logic path. The 2026 AML governance rules require that kind of rationale for any AI-driven decision on a federal record. Cortex delivers it by construction — no "explainability wrapper" bolted on top.
On defense AI benchmark, Cortex improved its own score 46% in 12 hours of unsupervised operation — autonomously finding gaps and patching them. In the SatCam context, that means the system gets better at the cases it was initially weakest on, without a human telling it where to look.
Traditional model: too big for edge — need to distill to a smaller, weaker version.
Cortex: runs completely on edge.
Traditional model: cannot retrain in the field; degrades as the world changes.
Cortex: learns in place, every session.
Traditional model: black box — hard to justify a decision on a federal record.
Cortex: explainable by construction.