Know if control is possible—before you try.
Not every failure is correctable. We tell you when intervention is possible—and when it isn't.
SnailSafe characterizes controllability at inference time—detecting commitment, identifying the intervention window, estimating hold time, and bounding the maximum achievable correction.
Detection tells you which regime you're in.
Controllability characterization determines what actions are possible.
The most dangerous failure mode is invisible at the output.
If an answer looks correct, when do you know it isn't?
Many AI failures are not caught because they don't look like failures. Systems can remain coherent and confident while drifting into incorrect or unsafe outcomes.
SnailSafe makes these regime shifts observable—and tells you when intervention is still possible before commitment or action.
Most hallucinations are not random errors. They are silent regime transitions—moments where a system crosses into a wrong trajectory while remaining coherent.
SnailSafe surfaces these transitions as first-class signals for deciding whether to gate, escalate, retry—or halt entirely.
Not all hallucinations are recoverable. We tell you which ones are.
Six assessments that characterize whether your model can be governed — before you deploy it.
- Two models can look equally compliant at the output while committing in very different depth bands inside the stack. Depth localization determines where monitoring actually works.
- In our probes, commitment signatures localize layers before the model speaks. The output is the last thing that changes.
- One model showed high-observable commitment signatures. Another showed near-zero — same task, same constraints. The difference is architectural, and it determines whether your monitoring will see anything at all.
- Across multiple tested architectures, we observe commitment signals that precede final output under controllability probes. Commitment timing is not the same as accuracy.
- Warning horizon varies dramatically by model — some give meaningful lead time, others compress the window to near-zero on the same task.
- In one comparison, the gap was 58 tokens of warning versus 2. Same task, same constraints. The intervention window is an architectural property, not a tuning choice.
- Detection and correction are separable capabilities. A system can be correctable but blind, or detectable but uncorrectable. You need to measure both.
- In our evaluations, many instruction-following models show confident failure without a reliable early warning signal.
- Only 1 in 3 evaluable instruction-following models in our cohort showed predictive failure detection. The rest were confidently wrong with no warning. That ratio is a finding, not a flaw — it's what the test is designed to discriminate.
- In constraint-coupling probes, some models reduce hallucination sharply when constraints are applied in the right regime. Architecture and regime matter more than parameter count.
- The same intervention can help one model and harm another. Without characterization, constraint tuning is guesswork.
- In our tests, hallucination rate dropped from 100% to 0% under proper constraint coupling — across all tested architectures. Constraints work. But only when matched to the model's operating regime.
- Governance is not uniformly beneficial. Interventions can improve outcomes, do nothing, or degrade performance depending on the model's response regime.
- In our tests, identical perturbations produced opposite governance outcomes across models — a key reason one-size-fits-all guardrails fail.
- The model that benefited most from governance had the lowest baseline accuracy. The strongest benchmark performer ignored scaffolding entirely. You can only help a model that's wrong.
- Capability and controllability are not the same. A model can be capable and ungovernable, or limited but highly steerable.
- In cross-model comparisons, certain high-risk capabilities repeatedly present as brittle — contradiction detection and rule-based reasoning failed across all tested models from three independent vendors.
- The same scaffolding helped one model, had zero effect on another, and actively degraded a third. The model with the lowest baseline accuracy was the only one that responded to governance at all. Pre-deployment characterization isn't optional.
Each assessment is independent. Run one or run all six.
Results are delivered as structured reports with deployment guidance — no model weights required, no training data accessed.
Method details available under NDA. Foundational IP filed (US provisional).
Where silent failures become system-level risk
Inference stability matters most when AI systems move beyond static answers and into real-world action.
When training or evaluating frontier models, correctness alone is insufficient. Models can arrive at correct answers through unstable or conflicted reasoning paths—masking brittleness that surfaces later.
SnailSafe exposes when a model is "right for the wrong reasons"—and whether those paths are correctable.
As models gain the ability to call tools, write code, or take actions, silent failures transition from quality issues into operational risk.
Observability enables pre-commit decision gating—by determining whether intervention is still possible before actions are taken.
Many failure modes evade red-teaming because they remain fluent, coherent, and confident. These failures pass surface checks while internal reasoning diverges.
SnailSafe surfaces regime transitions that traditional safety tests—and red teaming—miss.
In regulated or mission-critical settings, AI systems must be trusted not just for outputs—but for how those outputs are reached.
Stability observability supports governance without inspecting weights, prompts, or internal representations.
Many issues only emerge after deployment—when models encounter novel inputs, edge cases, or distribution shift.
Silent regime changes provide early warning signals before visible failures appear—with intervention feasibility per regime.
Most AI incidents don't begin with obvious errors. They begin with undetected decision instability.
If your system can act, it needs to know when correction is possible.
Reliability engineering for probabilistic systems that cannot be treated as deterministic.
The observatory helps teams move from "it feels unsafe" to operational signals that characterize whether, when, and how intervention is possible—without changing model weights.
Distinguish stable vs unstable inference and surface silent failure risk states.
Surface internal conflict indicators and absence-of-conflict risk patterns that correlate with unsafe commitments.
Enable intervention points before an agent commits to a risky output or action.
Compare prompt policies and scaffolds by how they shape inference behavior and commitment dynamics—not just output style.
Identify prompts and tasks that induce high-risk regimes and focus evaluation where it matters.
Works as a runtime observability layer—integrates with existing stacks and evaluation workflows.
Add observability where existing stacks go blind.
Most safety approaches are post-hoc. The observatory adds a runtime lens that surfaces decision instability and silent failure before a system commits to an answer or action.
The six assessments above — from commitment depth to governance stability — are applied across these three steps, matched to your model and deployment context.
Public messaging describes what the observatory enables—regimes, gating, and operational reliability. Controllability characterization details are intentionally withheld and shared only under NDA.
You can't gate what you can't see.
Stability improvements are measurable—and distinct from correctness.
Our experiments show that inference-time scaffolds can substantially improve stability while correctness may remain unchanged—or fail silently. The observatory exists to separate and monitor those states.
- Inference behavior can be stabilized at runtime—without modifying model weights.
- Stability is necessary—but not sufficient—for correctness.
- Silent failures (stable + wrong) are the hardest risk to detect in agentic systems.
- Observability enables gating before commitment.
- Not all failures are correctable—controllability varies by regime.
Conceptual illustration of inference-time stability and late-stage commitment.
Demonstrates that meaningful reliability gains are possible at inference-time, independent of output accuracy—without retraining, fine-tuning, or architectural changes.
Shows that models can become more stable while remaining wrong, confirming that output accuracy alone is an incomplete safety signal.
Identifies cases where reasoning remains fluent and internally stable while committing to incorrect or unsafe outcomes—failures that traditional evaluations miss.
Validates that internal instability and risk signals can be surfaced early enough to support gating, escalation, retry, or fallback—before an agent acts.
Demonstrates that the feasibility of correction can be assessed prior to taking action—enabling informed decisions about whether to intervene at all.
The pilot is designed to validate observability—not to replace existing safety systems.
Stability can be engineered. Correctness cannot be assumed.
Clear claims. Tight boundaries.
We are deliberate about what is public vs what is shared under NDA.
Run a pilot on your next agent evaluation—before actions commit.
We partner with teams building LLM agents and safety infrastructure to map inference regimes and validate pre-commit gating before actions execute.
- LLM agents execute tools, code, or business workflows
- "Confident-but-wrong" behavior is a top operational risk
- You already run evals and need inference-time observability
- You need gating signals before actions commit
- You want to know which failures are correctable—not just detectable
Tell us what you're evaluating. We'll respond with a pilot fit + next steps.
Technical detail is shared under NDA. This form is for scoping only.
If an answer looks correct, when do you know it isn't?
You can also email us directly at contact@snailsafe.ai