Skip the guesswork.
Developer tools for neural programming.
Diagnose and fix your models by looking at AI internals, not just surface level benchmarks.
Long development cycles, long tail failures, and wasted labeling time are inevitable when developers lack rigorous diagnostic tools to understand exactly what's happening in their model.
Give your scientists and engineers the tools to properly conduct hypothesis-driven experimentation. Integrate a single line from our SDK to get actionable fixes on why those batch effects are still dragging performance down.
Build a comprehensive thesis for exactly how and why your model behaves the way it does by accumulating the insights your researchers make through experimentation.
Go from weeks of debugging to hours.
Give your scientists and engineers the tools to properly conduct hypothesis-driven experimentation. Integrate a single line from our SDK to get actionable fixes on why those batch effects are still dragging performance down.
Build a comprehensive thesis for exactly how and why your model behaves the way it does by accumulating the insights your researchers make through experimentation.
Go from weeks of debugging to hours.
1.
Catch spurious correlations before they become production impacting.
2.
Causally trace model outputs to human-interpretable features inside the model.
3.
Build a constantly improving thesis of how your model behaves.
Rigorous science for model building.
No more witchcraft in machine learning. This has always bothered us as AI researchers. Ablation tests are second class citizens. 2% performance gains on benchmarks are "good enough" for publications (we're guilty of this too). As an industry, we've abandoned rigorous processes because we either don't think it's possible or assume it's more efficient to try everything until something finally sticks.
We believe machine learning should be as principled as software development — benchmark, debug, fix, and evaluate. We should be making trade-off decisions, not wild guesses. Like scientists in other domains, we should employ the scientific method. Learnings from experiments should contribute to a holistic understanding of why models behave the way they do, not get discarded as failures. Seeing exactly how your model's internal evolve over time is critical — and it's not just interpretability for the sake of knowing. Understanding what happens inside your models when you change data, architecture, and hyperparameters is the single most important thing we can do to accelerate model improvement.
Tessel is a research company giving AI model developers clarity on exactly how to fix the unfixable problems in their models. We use mechanistic interpretability for iterative improvement, helping you build neural representations that deliver safer, more robust, and predictable behavior.
We believe machine learning should be as principled as software development — benchmark, debug, fix, and evaluate. We should be making trade-off decisions, not wild guesses. Like scientists in other domains, we should employ the scientific method. Learnings from experiments should contribute to a holistic understanding of why models behave the way they do, not get discarded as failures. Seeing exactly how your model's internal evolve over time is critical — and it's not just interpretability for the sake of knowing. Understanding what happens inside your models when you change data, architecture, and hyperparameters is the single most important thing we can do to accelerate model improvement.
Tessel is a research company giving AI model developers clarity on exactly how to fix the unfixable problems in their models. We use mechanistic interpretability for iterative improvement, helping you build neural representations that deliver safer, more robust, and predictable behavior.
