Fultonbio provides AI-driven cloud platforms that analyze biomedical data to support clinical decision‑making.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare providers often lack rapid, data-driven tools to accurately diagnose cancer, predict effective treatments for bacterial infections, and assess genomic factors influencing longevity, leading to delayed interventions and suboptimal patient outcomes.
Solution
Fulton Bio offers AI-powered platforms that apply machine learning to biomedical data for three core use cases: Katechon delivers diagnostic and monitoring capabilities for cancer detection; Catalysis predicts optimal therapeutic strategies against bacterial infections; and Genesis provides genomic profiling to identify longevity‑related interventions. Each platform ingests clinical, molecular, or genomic inputs, runs validated predictive models, and outputs actionable insights that clinicians can integrate into treatment planning. The solutions are delivered via cloud‑based services with secure data handling, enabling scalable deployment across hospitals, research institutions, and specialty clinics.
Target Audience
Primary customers are oncology centers, infectious disease specialists, and precision‑medicine research groups that require AI‑enhanced diagnostics and treatment recommendation tools.
Features
- Katechon: deep‑learning models trained on imaging and pathology data to detect cancer presence, stage disease, and track tumor progression over time
- Catalysis: antimicrobial resistance prediction engine that recommends effective drug regimens based on bacterial genomic signatures
- Genesis: comprehensive genomic analysis pipeline that highlights variants linked to age‑related health risks and potential longevity therapies
- Cloud-hosted analytics with encrypted data transfer and storage to ensure patient privacy and regulatory compliance
- API integration points for electronic health record systems and research data platforms
- Continuous model updates driven by curated biomedical datasets to maintain clinical relevance