The MRI for AI.
Deep Neural Inspection reads the model's internal state during inference, in real time, so the answer never reaches the user as the wrong one.
Deep Neural Inspection, in one paragraph.
Most AI security and observability products read inputs and outputs. Strings going in, strings coming out, pattern-matched at the boundary. That approach works against attacks the industry already named, and breaks against everything else.
Deep Neural Inspection works differently. Realm extracts interpretable signals from a running model's internal state and uses those signals to classify intent in real time. The simplest version: we extract the model's reasoning as it forms, and decide from it before it ships.
The buyer outcome: every prompt, every response, evaluated against the model's actual reasoning, not its surface text. The 90% of failures that pattern matching never sees, exposed and stopped.
The research is public. The product is Realm's.
Mechanistic interpretability has been an active field at Anthropic, Google DeepMind, OpenAI, and the broader safety community for years. Landmark work on features, circuits, and sparse autoencoders has been published openly. The science tells us features exist inside a model's activations, that circuits connect those features, and that you can in principle read them.
Realm's contribution is not the science. The science is public. Realm's contribution is the engineering: a purpose-built classifier that reads those features in real time, in production, against OpenAI, Anthropic, Gemini, and self-hosted models, with the latency and cost profile a Fortune 500 buyer can deploy at scale.
Built on the same interpretability research foundation used at Anthropic, Google DeepMind, and OpenAI.
The foundations are public science. Deep Neural Inspection is Realm's.
The buyer outcome: the credibility comes from the same labs that built the field. The defensibility comes from what Realm engineered on top.
MRI vs. metal detector.
Pattern matchers
Like a metal detector at the door.
Regex, keyword filters, and content-moderation APIs match shapes in text. Defeated by rephrasing in minutes. Sees the surface, not the substance.
Deep Neural Inspection
Like an MRI. Sees inside.
Reads the model's reasoning trajectory in real time. Catches intent before the response commits. Generalizes across rephrased attacks.
The buyer outcome: every novel jailbreak, every indirect prompt injection, every multi-turn manipulation attempt that pattern matching misses, caught at the model's reasoning layer rather than at the input or output string.
What DNI sees during inference.
REFUSAL MECHANISMS
The internal signals that fire when a model is about to refuse, deflect, or hedge. Distinguishes genuine refusal from compliance performance, when the model is "playing safe" while quietly cooperating with a manipulation attempt.
The buyer outcome: Catch the jailbreak that looks like a refusal but is not one.
POLICY ADHERENCE SIGNALS
The features that activate when a model is operating inside vs. outside a defined policy boundary. Generalizes across rephrased attacks because the signal lives in the model's interpretation, not the user's wording.
The buyer outcome: Bring your hazard categories, regulatory definitions, and red-team test sets. DNI enforces them without retraining when the attack changes shape.
CONCEPT ACTIVATIONS
The internal representations of concepts the model is reasoning about (financial risk, PII, brand, harm, regulated content), even when the output text does not surface those concepts.
The buyer outcome: Catch the response that is about to leak sensitive data before the data appears in the output.
DECEPTION MARKERS
Internal signals that correlate with the model knowing one thing and saying another. The most sophisticated attack class DNI catches.
The buyer outcome: When the model is about to confidently assert something it internally flags as uncertain or false, you see it before the user does.
What DNI is not.
Not LLM-as-a-judge
Slow, expensive, hallucinates. Non-deterministic. The same input can produce different outputs run to run.
DNI uses purpose-built deterministic classifiers on interpretability features.
Not pattern matching
Regex, keyword filters, and content-moderation APIs match shapes in text. Defeated by rephrasing within minutes.
DNI reads the reasoning layer where intent actually lives.
Not bolt-on I/O classification
Generic text classifiers can only catch what they have seen. Limited generalization across rephrased attacks.
DNI classifies on internal features that generalize across surface text.
DNI is purpose-built deterministic classification on interpretability signals. The same input produces the same output, every time, with token-level reasoning per decision.
The buyer outcome: the only platform whose decisions are reproducible in front of a regulator, an auditor, and your own legal team.
Frequently asked.
How does DNI access the model's internal state? Does Realm need model-provider cooperation?
For self-hosted open-source models (Llama, Mistral, Phi), DNI runs directly against the activations during inference. For closed commercial models (OpenAI, Anthropic, Gemini), DNI runs as a purpose-built classifier on representations Realm extracts from adjacent inference paths. No private model-provider partnerships required.
What is the latency overhead?
Under 50 ms at p95 for the detection decision. DNI runs in parallel to the model call, not in serial, so user-facing response time is unchanged.
How does DNI generalize to attacks the team did not see during training?
Because DNI classifies on the model's interpretability features rather than on surface text patterns, it generalizes across rephrased attacks, novel jailbreaks, and indirect injection variants the training set never contained. Coverage detail in your demo.
What is Realm's IP position?
The interpretability research foundation is public science. Realm's IP is the engineering: the purpose-built classifier architecture, the production runtime, and the training and policy tuning systems that turn published research into a deployable enterprise product. The foundations are public science. Deep Neural Inspection is Realm's.
Is DNI the same thing as the mechanistic interpretability work coming out of Anthropic, DeepMind, and OpenAI?
Same research foundation. Different artifact. Those labs use interpretability research to make their own models safer in training and post-training. Realm uses the same research foundation to make any production model safer at runtime, against the actual traffic an enterprise sees.
See it read your AI.
30-minute working demo. Bring the model and the failure mode you cannot currently catch.