Measure what models do inside, not just what they say.
Infrastructure for Mechanistic Interpretability. Use dictionary learning and sparse autoencoders (SAEs) to audit internal activations and monitor model behavior at scale.
Interpretability as Governance
Peer inside the "black box" of artificial intelligence.
The Status Quo
Traditional evaluation relies on benchmarks and outputs. But fluency is not reliability. Mechanistic interpretability analyzes internal activations to extract discrete concepts before they reach the surface, turning "trust me" into "here is the evidence."
The NeuronLens Way
NeuronLens builds the production discipline for interpretability: measurable signals, continuous monitoring, and controlled intervention. We help frontier labs and enterprise teams monitor internal feature drift and ensure safety in high-stakes deployments.
Comprehensive Suite of Interpretability Tools
Everything you need to analyze, monitor, and influence model behavior.
Reasoning Lens
See whether a model's reasoning actually supports its answer — or if it's just generating confident-sounding text.
Agent Lens
Detect when an agent's internal signals disagree with the tools it actually calls — before it causes a problem.
Hallucination Lens
Flag claims the model makes without internal knowledge to back them up, in real time.
SLM Lens
Understand where a fine-tuned model is strong and where it struggles — so you can fix it, not just retrain it.
Trading Lens
Inspect how a model processes financial data and validate that its signals are grounded, not spurious.
Search & Steer
Find the internal features that drive any behavior and adjust them directly — no retraining required.
This is beyond just output monitoring
Why mechanistic interpretation is the future of AI safety.
Standard Evaluation Tools
- Relies on behavioral testing (black box)
- Cannot predict failure modes before they happen
- No causal understanding of "why"
- Limited to input-output correlation
NeuronLens Approach
- Direct inspection of internal activations
- Predictive failure detection via circuit analysis
- Causal tracing of features to outputs
- Precise steering with activation patching
Our Research
Technical Explorations
Deep dives into the mechanics of neural representations.
Sparse Autoencoders for Transcoding
Extracting interpretable features across transformer layers using dictionary learning.
Attributing Model Behavior to Features
Causal interventions to verify that extracted features actually drive model outputs.
Scaling Interpretability to Frontier Models
Engineering challenges and solutions for running SAEs on 70B+ parameter models.
Journal & Dispatches
Updates on our progress, product releases, and technical explorations in machine learning interpretability.
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.
Ready to master model clarity?
Join leading research labs and enterprise teams who trust NeuronLens for mechanistic visibility into their language models.