Model-Internal Intelligence
Look inside AI. Unlock hidden signals.
NeuronLens discovers internal concepts and turns them into deployable products for agent oversight, safety, and targeted model repair.
Not just what models say - what forms inside them.
The surface shows symptoms. The inside holds answers.
Prompts, outputs, and traces capture what a model said. They rarely surface the internal signals that caused the behavior - or the latent knowledge waiting to be discovered.
Outputs are shallow
Final answers hide the concepts that shaped them.
Failures stay one-off
Teams patch incidents instead of creating reusable controls.
Signals stay trapped
Useful model knowledge remains inside the model.
Surface signals show symptoms. Internal signals reveal mechanisms.
Discover. Control. Design.
Discover
Find internal features, concepts, and hidden signals
Every discovery becomes evidence you can act on, explain, and trust.
Control
Monitor and intervene during runtime
Internal signals become policy gates before failure fires.
Design
Repair and design models through internal evidence
Target the source behavior, not just the output symptom.
Control this run. Design future runs.
Products
Product Verticals
Agent Lens
Internal oversight for tool-using AI — catch risky actions before they fire.
Safety Lens
Detect grounding risk, prompt injection, and unsafe output from inside the model.
Model Repair Studio
Fix recurring model failures with targeted internal repair.
Powered by a research engine for activation analysis, sparse features, probes, and causal validation.
Our Research
Technical Explorations
Deep dives into the mechanics of neural representations.
Mapping the Concept Space Inside Language Models
How we used sparse features, auto-interpretability, and steering experiments to turn hidden activations into searchable, testable, and steerable signals.
Training Sparse Autoencoders That Produce Useful Features: A Practical Guide
A practical guide to training sparse autoencoders across model families, covering frameworks, failure modes, evaluation metrics, and case studies.
Labeling domain specific features of Sparse Autoencoders: Practical Strategies
From feature discovery to label fidelity in large language models (Nemotron + Llama case study)
From the Team
Essays, opinions, and updates on interpretability, AI safety, and what we're building.
Interpretability as Runtime Governance
From "Understanding Neurons" to Measurable Control Systems
Why Interpretability Stalled - and How It Gets Unstuck
The real bottlenecks that slowed interpretability progress, and what's quietly starting to change.
The Illusion of Chain-of-Thought Transparency
Why reading reasoning text tells you very little about what models actually think.
See what's driving your model.
Get early access to NeuronLens. We work with a small number of research and enterprise teams - reach out to start a conversation.