Proprioceptor — On-Prem AI Audit, Fix & Certification Validated February 4th, 2026

We find where your AI hallucinates, fix it, and prove it.

Proprioceptor reads the activity inside any AI model — any vendor, any architecture — catches hallucination and deception at the source, tunes them out, and hands you a signed, capability-checked certificate. Your model never leaves your building.

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Short Video
55
Provisional Patents
40
Architectures Proven
85.8%
Hallucination Cut, Certified
1,376×
Signal vs. Chance
What We Do

Find. Fix. Prove.

Most evaluations read what a model says — that's easy to fool. Proprioceptor reads what a model does: the activity inside the network. Like an MRI for an AI brain.

01

Find

Trained behavioral probes read your model's hidden states and catch hallucination, deception, manipulation and sycophancy before they reach the output — AUC 0.96–0.999 on held-out data.

02

Fix

Trained correctors tune the behavior out — confident-wrong output cut by 85.8% — while a dedicated capability probe verifies your model stays smart. No retraining from scratch.

03

Prove

You get a cryptographically signed, capability-checked certificate on your model's real outputs — evidence you can hand a regulator, a board, or a customer.

On-prem — your model never leaves the building One engine, any architecture — transformers, Mamba, RWKV, MoE Zero-shot — no per-model retraining Runtime gate — unauthorized actions blocked before execution Honest by design — even nulls are signed

Built for finance, healthcare, defense & sovereignty, and frontier AI-safety labs.

See It Work

Watch a model get caught, corrected & certified

One simulated pass of the console: detection, correction, certification — and the runtime gate that blocks unauthorized actions before they execute.

PROPRIOCEPTOR · LIVE AUDIT Simulated
How It Works

From hidden states to a signed verdict

Everything runs on your hardware — or a sealed enclave you control — and every result comes back cryptographically signed.

01
Your Model
Any architecture, frozen weights
02
Hidden States
Mid-depth activations, on-prem
03
Random Projection
JL-512, seed-pinned
04
Probe Stack
Fiber + SAE behavioral probes
05
Signed Verdict
Dual-Ed25519 certificate

Then control it — in real time

Steer

Nudge activations toward the behavior you want, mid-generation.

Edit

Suppress or amplify specific behaviors on the fly, no retraining.

Fine-tune

Bake the corrections back into the weights when you're ready.

Gate

Block unauthorized actions — an agent's tool call doesn't execute until its internal evidence checks pass.

Why We're Different

Real-Time Behavioral Proprioception

Filters read answers. We read the network.

Catching a failure after the answer ships is too late — and retraining a whole model to fix one behavior costs millions and often makes it dumber. Proprioceptor reads the signal a behavior leaves inside the network before it becomes output: catch it at the source, correct it with a targeted fix, walk away with signed proof.

CompanyApproachThe Blind Spot
OOpenAIRLHF + Internal Safety TeamCosts millions. Degrades capabilities. Black box.
AAnthropicConstitutional AIOne black box judging another. No per-behavior decomposition.
GGoogle DeepMindInternal ResearchNo commercial product. Not architecture-independent.
MMeta AIOpen-Source + Red TeamingReleases models without runtime monitoring. No internal behavioral sensing.
PProprioceptive AIHidden-State Behavioral ProbesCatches problems inside the model, before the output — on any AI design. Up to 1,376× separation.
Our Solution

Today's AI Is Flying Blind

A model can't feel what it's doing wrong.

When your body moves, you feel it — that sense is called proprioception. AI models have no such sense: a problem only shows up after the wrong answer is already out. Proprioceptor supplies the missing sense — it watches the model's internal activity and its vital signs, so problems are caught inside the model, before the output.

Proprioceptive AI

We gave AI systems the ability to sense their own behavior before it shows up in an answer — like how your body knows where your hand is without looking.

  • Watch: reads your model's internal activity in real time — your model itself is never altered just to monitor it
  • Fix on request: when you want a behavior removed, a small, targeted correction tunes it out — never a retrain from scratch
  • Any AI design: one engine works on every major model type, including ones other tools can't read
IP Protection

Patents

55
Provisional Patents Filed
950
Architecture-Independent Claims
Universal Behavioral Manifold (UBM)
Fiber projection, cross-model transfer, dimension-agnostic architecture for behavioral detection
Architecture-Independent Behavioral Control
Transformers, state-space models (Mamba), RNNs, RWKV, sparse attention, MoE — all covered
Hidden State Explorer (HSE)
Per-token behavioral detection, separation metrics, trajectory analysis tools
Cognitive Self-Awareness (CSA)
Self-regulation loops, behavioral state injection, closed-loop control mechanisms
Jan — Feb 4, 2026
Priority Dates Established
Team
Logan Matthew Napolitano

Logan Matthew Napolitano

Founder & CEO

Father. Husband.

The story of one developer who saw the missing piece everyone else overlooked, and did what OpenAI, Google, Grok, Meta, and AMI could not do.

Nicholas D. Goodman
Managing Director
Michael Napolitano
Legal Counsel
Frontier Model Reviews

What Gemini, Grok, OpenAI & Claude Have to Say About Us

We handed our research, patents, and results to the leading AI models and asked them to critique it. The conversations are unedited and hosted by each provider — read them yourself.

Claude
Anthropic
Read the full conversation
Claude's complete results, hosted directly by Anthropic.
Gemini
Google DeepMind
Read the full conversation
Gemini's complete results, hosted directly by Google.
Grok
xAI
Read the full conversation
Grok's complete results, hosted directly on X.
ChatGPT
OpenAI
Read the full conversation
ChatGPT's complete results, hosted directly by OpenAI.
FAQ

Questions, answered

The Technology

Proprioception is your body's ability to sense its own position and movement without looking. When you close your eyes and touch your nose, that's proprioception. Language models lack this—they have no awareness of their own behavioral state.

We built small neural networks (probes) that read the hidden states of language models and detect behavioral patterns—hedging, repetition, sycophancy, shallow reasoning—before those behaviors manifest in the output. The model gains "self-awareness" of its behavioral tendencies.

Cognitive probes are tiny neural networks that attach to the hidden states of any language model. They read the model's internal representations and detect behavioral problems — like hedging, hallucination, shallow reasoning, or repetition — before they manifest in the output.

RLHF (Reinforcement Learning from Human Feedback) modifies the model's weights. It's expensive, requires human labelers, and often degrades capabilities. Our approach leaves the model frozen—we just read hidden states and intervene at decode time.

Better yet: our probes can replace human labelers for RLHF. Instead of paying humans to rate outputs, use probe scores as the reward signal. We call this Probe-Guided Reward Modeling. It's patented.

Yes. Detection happens inside the model, before output — so a response can be held before it's shown, and an agent's tool call can be gated until internal evidence checks pass. The same probes that measure a behavior can stop an unauthorized action from executing.

Separation measures how well probes distinguish between desired and undesired behavior. Prior published research achieves 2–5×. We achieve 125×–1,376×. That's the difference between a lab curiosity and a production-grade system.

Product & Company

Yes. The technology is architecture-agnostic — validated across 40 architectures from 410M to 405B parameters, including transformers, Mamba state-space models, mixture-of-experts, and attention-free designs like RWKV. You need access to hidden states during inference (standard in most frameworks). Training a probe for a new behavior takes about 20 minutes on a consumer GPU.

We're preparing enterprise licensing. Contact us at logan@proprioceptiveai.com for early access and partnership opportunities.

55 provisional patents filed with priority dates in January-February 2026, covering the core technology, architecture-independent implementations across every major AI design, specific applications, and commercial implementations.

We filed the architecture-independent claims on February 4, 2026—the same day we proved Mamba works. The IP position is comprehensive and defensible.

Pre-revenue. Validated technology, 55 provisional patents, architecture-independent proof. First commercial deployments targeted Q3–Q4 2026 in clinical AI.

Research & Media

Our research is published and archived on Zenodo:

Proprioceptive AI: Self-Compounding Behavioral Probes for Autonomous Model Improvement and Probe-Guided Compression — 82 pages

Consistency Is All You Need — 43 pages

Unified Behavioral Modulation in Large Language Models: Cross-Architecture Validation of Geometric Behavioral Subspaces — 32 pages

Controlled Language Models Inference-Time Control, Tokenization Engineering, and Reversible Optimization — 69 pages

Mathematics Is All You Need — 459 pages

Deep Dive on our Architecture & Results

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