ReLup offers a scalable platform that enables individuals, healthcare organizations, and researchers to monitor cognitive health over time and detect subtle signs of decline earlier. By combining digital assessments, multimodal data collection, and AI‑powered analysis, the system provides actionable insights that support preventive interventions and healthier aging. The NeuroLup app serves as the user‑facing interface for ongoing monitoring, while the NeuroLup AI engine processes the data to flag meaningful changes.
Funding
Funding not disclosed
Founders
Product
Problem
Cognitive decline is frequently identified only after noticeable symptoms appear, limiting opportunities for early intervention and prevention. Existing monitoring methods are often episodic, clinic‑based, and lack scalability for continuous, population‑level assessment.
Solution
ReLup offers an AI‑driven platform that enables continuous monitoring of cognitive health for individuals, healthcare providers, and researchers. Users begin monitoring with the NeuroLup mobile app, which delivers standardized digital assessments and captures multimodal data such as behavioral performance, speech, and sensor inputs. Collected data are transmitted to the NeuroLup AI engine, which applies multimodal machine‑learning models to detect subtle patterns indicative of early cognitive decline. The system generates longitudinal health scores and flags meaningful changes, allowing clinicians and users to initiate preventive actions sooner. All analytics are hosted in a secure cloud environment, and results can be accessed through a web dashboard or integrated into existing health‑record systems.
Target Audience
Primary users are adults seeking proactive brain‑health monitoring, healthcare organizations that manage patient populations at risk of dementia, and academic or commercial researchers studying cognitive aging.
Features
- NeuroLup mobile app provides regular, self‑administered digital cognitive assessments and passive data capture
- Multimodal AI engine combines behavioral, speech, and sensor data to produce early‑detection risk signals
- Automated longitudinal tracking visualizes individual cognitive trajectories over time
- Secure cloud‑based analytics pipeline delivers encrypted data storage and processing
- Web dashboard presents actionable alerts, trend graphs, and exportable reports for clinicians and researchers
- API integration enables connection with electronic health record systems and research data platforms