Thelumos offers an AI platform that lets health‑tech developers fine‑tune, evaluate, and align large language and vision models for clinical use. By combining supervised fine‑tuning with reinforcement learning from human and AI feedback and scalable rubric‑based scoring, the platform produces models that meet safety, accuracy, and regulatory standards for medical applications.
Funding
Funding not disclosed
Founders
Product
Problem
AI models applied to health and life‑science domains often suffer from insufficient accuracy, lack of safety guarantees, and limited mechanisms for systematic evaluation and alignment with clinical standards. This hampers their adoption by healthcare providers, regulators, and research organizations.
Solution
Thelumos provides a specialized AI platform that enables developers to fine‑tune, evaluate, and align large language and vision models for health applications. By combining supervised fine‑tuning (SFT) with reinforcement learning from human and AI feedback (RL/HF, RLAIF), the platform iteratively improves model behavior toward clinically relevant objectives. Large‑scale rubric scoring performed by AI evaluators augments human judgments, delivering high‑coverage, quantitative assessments of model outputs. The resulting models are calibrated for safety, reliability, and regulatory compliance, allowing partners—including foundational AI labs, health‑tech startups, and government agencies—to deploy trustworthy AI solutions in medical contexts.
Target Audience
Primary customers are AI research labs, health‑technology companies, pharmaceutical and biotech firms, and governmental health agencies that need to develop or certify safe, high‑performing AI models for clinical and life‑science use cases.
Features
- Supervised fine‑tuning pipelines tailored to health data, supporting domain‑specific adaptation of foundation models
- Reinforcement learning from human and AI feedback (RL/HF, RLAIF) to shape model behavior toward clinical safety and accuracy goals
- Scalable rubric‑based evaluation framework that merges human expert judgments with automated AI scoring for comprehensive model assessment
- Integrated traceability of model decisions and feedback loops to support auditability and regulatory review
- APIs and tooling for seamless integration of the platform into existing AI development workflows of labs, startups, and public health programs