We find where healthcare AI breaks
Your model passes benchmarks. We test if it's safe for patients.
Core Thesis
We design and produce healthcare datasets that test how models behave under real clinical conditions — including uncertainty, incomplete information, and evolving patient states.
Our focus is not on what models know, but how they reason.
Our Approach
Protocol-based dataset generation
Failure-driven design
We target scenarios where models produce plausible but incorrect decisions.
Real clinical complexity
Cases include ambiguity, conflicting signals, and time-sensitive decision-making.
Structured evaluation
Each task includes expert-built rubrics focused on reasoning quality and safety.
Why MAKZ
Quality Above All
As models improve, the bottleneck is no longer data volume — it's whether the data actually exposes where models fail.
We design datasets that go beyond textbook scenarios and pattern matching. Our focus is on:
- real-world clinical ambiguity
- incomplete and conflicting information
- edge cases and failure modes
Every line of data is built to test reasoning, not recall — and ultimately answer one question:
Does this make the model better?
Deep Experience in Healthtech & AI
We've spent over 6 years working at the intersection of healthcare and AI.
This includes:
- supplying clinicians into healthtech and AI environments
- working closely with how models are trained, evaluated, and improved
- understanding where models succeed — and more importantly, where they break
We don't approach this as a data vendor, but as a partner focused on improving real-world model performance.
10,000+ Clinician Talent Pool
We have built a network of over 10,000 clinicians, including specialists across key domains such as oncology, cardiology, and paediatrics.
Many of our clinicians:
- have experience working with frontier AI systems
- understand evaluation frameworks and model behaviour
- can contribute beyond annotation into reasoning, critique, and refinement
This allows us to deliver high-quality, expert-driven data at speed — without compromising on depth.
The Gap
Many models perform well on standard benchmarks but fail in real-world clinical settings. We create the data needed to close that gap — improving safety, reliability, and real-world usefulness.