We provide a 360° AI evaluations for health, chemistry, and biology AI models.
We provide a 360° AI evaluations for health, chemistry, and biology AI models.
Most evaluations are generic, inaccurate, and built on irrelevant data — making it hard to decide what’s next.
Just as A/B tests guide software updates, evaluations should drive model development. But typical evaluations are too broad and often based on outdated or irrelevant data, offering limited clarity on real-world performance.
Our approach pinpoints exactly where your model needs improvement, clearly highlighting the best path forward.
Tools we use
Dataset Validation
Training datasets are too big to spot hidden biases or mislabeled data.
Training data often contains hidden biases that distort model predictions. If left unchecked, these biases lead to systematic errors and unreliable real-world performance.
We cut through the noise. Our validation process scans your dataset for biases, inconsistencies, and blind spots, ensuring your model learns from clean, representative data.
Example / Skin cancer detection app
A skin cancer detection model claimed 90% accuracy. We uncovered a flaw: its dataset was made up of lighter skin tones, making it unreliable for darker skin. We flagged the issue, rebalanced the data, and improved accuracy across all demographics.
Custom Metrics
Metrics like F1 and Precision help you build a model, but not a product.
Standard metrics don't help you turn your model into an actual product.
We break down your pipeline to understand how each stage affects your model’s performance. We then design custom metrics that capture its behavior in real-world customer use cases.
Example / Agents to automate GP operations
Additional set of metrics to track mentions of prescribed medication and cross-border drug interchangeability in a conversation based model.
Multi-level Benchmarking
Holistic benchmarks miss the root cause. We dig deeper.
Public benchmarks are holistic but don’t pinpoint where your model fails. Many were part of its pre-training data, making their evaluations redundant. Worse, they rely on textbook examples rather than real-world frontline data.
We go deeper. Our evaluations break performance into clear buckets, showing exactly what’s missing so you can fix it.
Example / Pathology detection in radiology hardware
We went from MedQA to subcategory in radiology. Then to image types - X-rays and MRIs. We then focused on a body part, to find that chest X-rays where causing the issue. Slashed the data needed for critical improvements by 25x, reducing it to just ~17k labels.
Full-cycle RLHF
Non-specialized players cannot offer exceptional quality.
We're a team from Harvard, Oxford, and Stanford medical schools with domain-specific taxonomies to track skill-level quality, not just average result.
We design use-case-specific rubrics and dynamic scoring systems that adapt to context.
Sourcing experts from top universities and hospitals, whether using RLHF or DPO, our top experts ensure exact model alignment.
Example / Agents startup that runs hospital operations
Realigning model to provide crisp answers when there's a risk to life. Creating a bank of 37 life-threatening edge-cases.
Pure RL
Human data is slow to obtain and gatekept by hospitals.
We train our own AI judges to run a proper RL. The results and turns are getting scored instantly, while a small percentage of scores go for a human review to ensure accuracy, alignment and continues improvements.
Example / Agents startup that runs hospital operations
Using AI as a judge to score patient-model communication to realign and spot any edge cases.
How to Start
- Intro Call to Align on the Use Case — Define the specific problem and goals.
- Approve Key Characteristics — Determine what matters most (e.g., accuracy, safety, comprehension).
- Design and Sign Off Rubrics — Develop evaluation criteria together.
- Create a 360° Evaluation Mix — Balance human evaluation, metrics, and benchmarks for full coverage.
- Run an Evaluation — Integrate multiple tools to pinpoint exactly where your model falls short.