Where comfort and dignity come first

Generate preference pairs that reflect real clinical reasoning. Evaluate response quality based on clinical accuracy, safety, and adherence to standard of care in real care settings—not just fluency. Typical outputs include ranked response pairs, detailed preference rationales, clinical accuracy scores, identification of hallucinated clinical facts

Transform complex clinical protocols into structured evaluation criteria your models can learn from. Convert unstructured clinical knowledge into measurable verification tasks. Typical outputs include structured rubrics, verification test sets, inter-rater agreement baselines, edge case documentation

Models fail in surprisingly human ways: a triage suggestion that downplays red‑flag symptoms, or a draft note that quietly invents an allergy. Probe for these failure modes, map them into patterns, and create concrete targets to harden guardrails. Typical outputs include categorized safety failures, severity scoring, reproduction steps, mitigation recommendations, adversarial test sets

Modern clinical data spans formats: progress notes reference imaging findings, waveforms explain treatment changes, pathology images inform diagnoses. Annotate text, images, audio, and signals together—the way clinicians actually integrate information. Typical outputs include cross-modal annotations, linked reasoning chains, multimodal quality scores, format-specific error analysis