Starseer | AI Platform — Runtime Assurance & Detection Engineering

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Interpretability-grounded detection

Core differentiator

Starseer uses mechanistic interpretability — activation analysis, circuit tracing, and behavioral probing — to reveal what AI models truly learn and do at inference, detecting hidden threats like backdoors and covert capabilities invisible to traditional signals.

Output monitoring alone

Industry baseline

Tools that monitor AI outputs can detect anomalies in what a model says or does — but they're blind to why, and blind to threats that produce normal-looking outputs deliberately. Behavioral monitoring is necessary. It isn't sufficient.

Pre-deployment through runtime

Platform vision

AI-Verify validates that approved models are the only ones running. AI-DE engineers the detections that fire at runtime. AI-EDR runs those detections against live endpoints and responds. Three products, zero handoff gaps — full coverage from source to production.

— Our Solutions

Validated, optimized,
and trusted AI.

Runtime Assurance by Design

AI systems must be safe and predictable in operation, not just compliant on paper. Starseer is built to assure AI behavior continuously in real-world, autonomous, and edge environments.

Behavioral Transparency

Understanding why AI systems act is essential for trust. Starseer delivers deep model and behavioral understanding to expose reasoning, decisions, and system-level behavior — not just metrics.

Detection Engineering First

Security, safety, and reliability start with detections that are designed, tested, and improved across the AI lifecycle. Starseer treats detection engineering as a core discipline, not an afterthought.

— AI-DE

Detection Engineering

Enable trusted AI operation through incident response, forensic root-cause analysis, ongoing detection tuning, and comprehensive evidence and audit trails. When something goes wrong, know exactly why — and close it for good.

Detection lifecycle

1

Design

Define detection logic against known threat patterns and AI risk categories

2

Test

Validate against red-team scenarios and known attack patterns before deployment

3

Deploy

Push approved detections to live AI endpoints as active runtime guards

4

Monitor

Track detection performance, alert volume, and false positive rates in production

5

Tune

Adjust thresholds and logic based on operational feedback and new threat data

6

Retire

Deprecate detections superseded by improved coverage or changed threat surface

↻

Lifecycle repeats on new threat intelligence, model updates, or incident findings

— What Teams Are Saying

Trusted by teams deploying
AI in production.

"We went from a stalled frontier model experiment to a production-ready agentic system in 30 days, fully offline, 90% cheaper, and entirely on our terms."

VP of Product · Enterprise Software Company

"From proof-of-concept to production in one month. Offline. No frontier API dependencies. We didn't think that was possible."

Jim Webster

Director of Federal Programs · SCYTHE

"Starseer didn't just solve our deployment constraints. They picked a better model than we were using and shipped something we actually trust in production."

Head of Engineering · Enterprise Technology Company