Knightingale is an independent clinical AI safety layer designed for organizations deploying AI into healthcare environments. It evaluates systems the way a vulnerable patient, hospital reviewer, or safety officer would experience them.
Independent deterministic validation before deployment and after major updates
A defensible record of every clinical rule tested, every failure identified and every risk quantified.
Evidence packages that support procurement, governance review and partner trust.
Knightingale follows a repeatable audit process that turns uncertain AI behaviour into documented clinical evidence.
Define the Al system, use case and clinically context to be reviewed.
Create clinically grounded prompts, edge cases, and escalation situations.
Compare outputs against verified clinically and compliance relationships.
Record missed escalations, unsafe claims, and severity with clear evidence.
Provide corrective actions and re-audit after material changes.
Knightingale is designed to produce outputs that decision-makers can use immediately across clinical, compliance, and commercial settings.
A structured assessment of the system, test scope, findings, and overall risk picture.
A clear list of identified clinical failure modes, severity, and exposure points.
Prioritised recommendations for fixing unsafe outputs and governance gaps.
Documentation suitable for boards, procurement teams, and enterprise partners.
A model for recurring review as the Al system evolves over time.
