Inference-time controllability characterization.
What We Do
SnailSafe characterizes inference-time behavior in probabilistic AI systems.
Most AI failures are evaluated only after an answer is produced. SnailSafe operates earlier — during inference itself — to determine whether a model's behavior is observable, controllable, or already committed before intervention is attempted.
This allows teams to distinguish between failures that are:
- correctable,
- ill-posed to correct, or
- already irreversibly committed.
The result is not more steering — but better decisions about when steering is meaningful at all.
The Problem We Address
Modern AI systems are probabilistic by design.
They do not fail uniformly — they fail by regime.
Some failures occur early, while inference trajectories are still fluid. Others occur after internal commitment, when additional data, retries, or steering only reinforce the same outcome.
Most existing approaches treat all failures as equally correctable.
They are not.
Without inference-time characterization, systems:
- apply correction when it is already too late,
- confuse instability with controllability,
- and escalate intervention without knowing whether it can succeed.
Our Approach
SnailSafe introduces an inference-time observatory that characterizes decision dynamics before output is finalized.
We measure:
- whether inference has entered a constrained, well-defined regime,
- when commitment occurs,
- how long intervention must persist to matter,
- and the maximum achievable correction for a given regime.
This characterization is architecture-independent, model-agnostic, and does not require access to model weights.
The observatory does not attempt to force correctness.
It determines whether correction is possible in the first place.
What SnailSafe Is — and Is Not
- an inference-time diagnostic layer,
- a controllability characterization system,
- a decision support tool for AI governance and safety.
- a training framework,
- a prompt-engineering toolkit,
- a replacement for infrastructure, RAG, or orchestration.
It complements existing stacks by telling them when intervention is worth doing — and when it is not.
Why This Matters
Detection alone tells you that something went wrong.
Characterization tells you what can be done about it.
By separating:
- observability from controllability,
- instability from irreversibility,
- and risk from actionability,
SnailSafe enables systems to:
- halt cleanly instead of compounding errors,
- escalate only when correction is feasible,
- and avoid reinforcing committed failures.
This is especially critical in high-stakes domains where continuing inference is worse than stopping.
Founder & Chief Architect
Gregory Ruddell
SnailSafe emerged from independent research focused on a single question:
What happens inside a model before it commits to an answer?
Rather than optimizing outputs, the work examined inference dynamics directly — identifying gated regimes, commitment events, and bounded controllability without relying on training access or model modification.
The result was not a new model, but a new way to reason about inference-time decision behavior and intervention feasibility.
Contact: contact@snailsafe.ai