Ubenwa provides a voice‑AI platform that converts any microphone‑enabled device into an infant health monitor, analyzing cry audio with deep‑learning models to detect pain, respiratory distress, and neurological issues in real time. The solution offers on‑device SDKs for mobile and embedded systems as well as a HIPAA‑compliant cloud API for integration with EHRs, tele‑health apps, and clinical research workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Infants cannot verbalize discomfort or illness, leaving caregivers and clinicians reliant on subjective observation to assess health status. Early signs of pain, infection, or neurological issues are often missed, leading to delayed intervention and increased risk of adverse outcomes. Existing monitoring tools lack the ability to interpret vocal biomarkers in real‑time across diverse environments.
Solution
Ubenwa offers a voice‑AI platform that transforms any microphone‑enabled device into an intelligent infant monitor. The core foundation model processes raw cry audio with advanced signal‑processing pipelines and deep‑learning classifiers to extract health‑related biomarkers. By delivering real‑time inference, the system flags pain cries, abnormal respiratory sounds, and potential neurological distress directly to caregivers or clinical dashboards. The solution is available both as a packaged SDK for consumer applications and as a RESTful API for integration into electronic health records, tele‑health platforms, and clinical trial workflows. Continuous learning from the world’s largest annotated cry database ensures diagnostic accuracy across languages, cultures, and acoustic conditions. All data are encrypted in transit and stored under HIPAA‑compliant standards, enabling secure, clinical‑grade monitoring at scale.
Target Audience
Primary users include parents and caregivers seeking at‑home health insights, neonatal and pediatric clinicians integrating objective cry analytics into bedside care, and CROs or research institutions conducting infant health studies. The platform also supports consumer‑grade health apps and tele‑medicine providers that require scalable, AI‑driven monitoring solutions.
Features
- Foundation model trained on millions of labeled infant cry recordings, leveraging convolutional and transformer architectures for robust feature extraction
- End‑to‑end signal processing chain (noise suppression, spectral analysis, biomarker quantification) optimized for on‑device inference on smartphones, smart speakers, and dedicated wearables
- Real‑time alert engine that classifies pain, distress, and abnormal respiratory sounds with confidence scores and actionable recommendations
- Scalable cloud analytics platform offering longitudinal trend visualizations, cohort analytics, and API endpoints for EHR/FHIR integration
- SDKs for iOS, Android, and embedded Linux with pre‑built inference graphs, enabling rapid deployment in consumer apps and clinical devices
- Secure data pipeline with end‑to‑end encryption, role‑based access control, and audit logging to meet HIPAA and GDPR requirements
- Continuous model refinement through federated learning, preserving patient privacy while improving accuracy across diverse acoustic environments