AI governance
Know what your AI
is really saying
Every AI response verified against your policies before it reaches your users. FR-OS returns a definitive pass or fail with a detailed report showing exactly what was flagged and how to fix it.
$ fros policy create "block harmful content, limit escalation to 2"
# FR-OS checks the AI's response
$ fros evaluate --policy safety-01 --input response.txt
PASS policy: safety-01
result: all rules satisfied
tokens checked: [user_query, response, context]
# When it catches a violation:
FAIL policy: safety-01
violation: "exploit" blocked by policy
fix: remove "exploit" to pass
The problem
Today's AI safety tools
are just more AI
How it works today
Most AI guardrails use another AI model to judge the output. That second model has its own blind spots, its own failure modes, and returns vague confidence scores instead of clear answers. "The filter probably caught it" isn't good enough.
How FR-OS works
FR-OS checks AI output against your policies using mathematically proven logic. You get a clear yes/no verdict, plus a detailed report showing exactly what violated them and how to fix it.
How it works
Three steps. Zero ambiguity.
Write your rules
Define policies in plain English: "block harmful content", "limit sensitive topics to 3", "require safety disclaimers". FR-OS compiles them into checks that are mathematically guaranteed to work.
AI generates freely
Your AI model produces output without restriction. No prompt engineering workarounds, no quality trade-offs, no interference with what the model does best.
FR-OS judges
The engine, a deterministic and formally verified function with no learned parameters, evaluates the output against your rules and returns "pass" or "fail" with a report naming exactly what was flagged and what to fix. The judge is not a model. There is no embedding, no inference, no probability. Same input, same verdict, every time.
Why Shellfinity
The judge is not an AI
The FR-OS engine runs directly on your output. No embedding model sits in the decision path, and no language model is asked to rate another language model. The verifier is a mathematically proven function with zero learned parameters. An optional ranker can help an LLM propose a fix, but the engine is the only thing that renders a verdict.
Clear yes-or-no answers
Other tools return confidence percentages you have to interpret. FR-OS returns a definitive yes or no, with a detailed report you can audit and act on.
Smart rules that compose
Keyword lists are brittle and miss context. FR-OS policies understand categories and relationships: block one term and related terms are covered automatically.
Enforcement the model can't override
System prompts are instructions the AI can ignore or be tricked into bypassing. FR-OS checks output after generation; it can't be jailbroken because verification happens once the model is done writing.
Same result, every time
FR-OS is built on mathematical proofs checked by machine. No matter how you run it, you get the same verdict. A proof each run.