Model-native assurance for AI-generated code
Safety and vulnerability signals read directly from a model's activations, on the same host as generation. No exfiltration surface. No external guard model.
No exfiltration surface
Nothing leaves the inference environment
Structurally harder to evade
Reads internal state an attacker confined to the input/output interface never sees
Complements V&V ↓
Details below
A measurable answer
An inline number your engineers can reason about, not a post-hoc review
Triage, not blanket review
Risk-scored diffs let reviewers and static analyzers focus effort where it's actually needed, instead of treating all AI-generated code equally.
Catches issues before the pipeline
The probe runs at generation time, before code is committed — fewer V&V cycles spent validating code that gets rewritten or rejected later.
A cheap pre-filter, not a shortcut
Static analysis and manual review still run in full — the probe only reorders what gets prioritized first, at near-zero added cost.
An added evidence artifact
Probe scores can be logged alongside existing verification records as a supplementary signal — this supplements your assurance case, it does not replace any part of it.
We haven't yet run this inside a live DO-178C-style or defense V&V pipeline — the above describes the intended integration points, not a measured cycle-time reduction.
See it on your own model
Talk to us about running a pilot on your stack — no retraining, no model swap-out required.
Talk to us