Grace Chang
CEO
Kintsugi
Kintsugi provides an API-first platform that analyzes voice biomarkers in speech to objectively identify and prioritize mental health challenges in real time. This technology integrates with telehealth and call center systems to offer practitioners quantifiable screening tools for timely intervention. The platform aims to scale access to mental healthcare by providing objective insights into what patients are not explicitly saying.
- Artificial Intelligence
- Digital Health
- Healthcare Technology
- Software Only
Updated 23 months ago
Funding
Raised to date
$30.9MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
+3Funding rounds are not available yet.
Founders
Product
Problem
Many individuals with clinical depression and anxiety go undiagnosed, as they may not explicitly report their struggles or exhibit obvious symptoms during routine checkups. Traditional mental health assessments often rely on self-reporting, which can be subjective and may not capture the full extent of a patient's condition. This can lead to delayed intervention and support for those who need it most.
Solution
Kintsugi offers a solution by leveraging machine learning and voice biomarker analysis to detect subtle indicators of depression and anxiety in patients' speech. By analyzing short audio clips, the technology identifies vocal patterns and linguistic cues that correlate with mental health conditions, even when individuals report feeling "fine." This allows healthcare providers to gain a more objective and comprehensive understanding of a patient's mental state, enabling timely intervention and personalized support. The platform integrates with existing healthcare systems, providing a seamless way to incorporate mental health screening into routine care.
Target Audience
The primary target audience includes healthcare providers, payers, and hospitals seeking to improve mental health screening and early intervention for patients.
Features
- AI-powered voice biomarker analysis for objective mental health screening
- Detection of depression and anxiety indicators from short speech samples
- Integration with existing healthcare platforms and workflows
- Identification of patients who may not self-report mental health struggles
- Quantifiable mental health assessment tool for practitioners