Real-world performance does.
but will they work in the hospital?
Beyond Benchmarks – A New Paradigm for Building Trustworthy Pathology AI
Build trust that converts
We track every model iteration and decode the black box, showing exactly what works, what fails, and why. Give hospitals the confidence they need with transparency that speaks their language.
Beyond vanity metrics
Robustness testing and bias detection that translates to real patient impact
Manage lifecycles
Track exactly how changes affect performance. Never break critical workflows your clients depend on
Scale your expertise
Compound insights across experiments. Build institutional AI knowledge that outlasts any single researcher
Beyond Benchmarks – A New Paradigm for Building Trustworthy Pathology AI
Bring Rigor to AI Buying Decisions
With AI, you've been stuck making gut decisions without the data you need—or avoiding decisions altogether because the risks feel too unclear. Tessel changes that. Evaluate AI like you evaluate everything else—with clear metrics, transparent performance data, and objective vendor comparisons. The best solution should win based on merit.
Measurable patient outcomes
Vet vendors on your hospital’s data, not academic benchmarks, before buying. All the while never letting your hospital’s data leave your facility.
Transparent procurement
Make AI selection as rigorous and evidence-based as any other RFP process, with clear performance metrics and objective comparisons between vendors.
Cost justification
Know exactly what AI will get right and wrong so you can run the most accurate ROI calculations.
Our Vision
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.
