Multilingual by design
Support natural conversations across Indian languages, regional dialects and everyday code-switching—subject to representative local validation.
AI voice analysis · WHO-aligned screening
Every patient deserves
Every patient deserves
to be heard.
NivaCare is exploring consented AI analysis of speech patterns together with mental health screening structured around WHO guidance. The capability is intended to surface possible changes for qualified clinician review—not diagnose a condition, assess an emergency or replace a clinical interview.
Consent-aware listeningVoice analysis can add another layer to the patient story.
Speech pacePause patternsPitch & energyRhythm over time
From voice to preventive attention
Listen for change. Respond with care.
In a governed pilot, voice-analysis signals may be considered with WHO-aligned screening questions and the patient's history. The result is not a diagnosis—it is investigational context that helps the clinician decide what deserves a conversation.
01Ask permissionThe patient understands what is analysed and chooses whether to participate.
02Hear naturallyThe patient speaks in the language, dialect or code-switching style that feels natural.
03Find changeAI agents compare speech patterns and screening responses with the patient's own baseline.
04Review togetherThe clinician sees explainable signals alongside physical health and longitudinal context.
05Connect supportConversation, follow-up and appropriate referral stay part of one care journey.
Built for India's many voices
Mental wellbeing should not depend on a PIN code or a language.
From a neighbourhood clinic to a remote consultation, NivaCare is designed to bring preventive mental health attention into the routine doctor-patient relationship across Tier 1, Tier 2 and Tier 3 India.
Closer to first contact
Bring a preventive mental wellbeing layer into general practice, chronic-care visits and telehealth—not only specialist settings.
Connected beyond cities
Help local clinicians recognise possible changes and connect patients to the appropriate next level of support.
A preventive signal—not a label
AI can notice a pattern. Only people can understand the person.
AI voice analysis is treated as one consented, investigational screening input—not a diagnostic result. Representative Indian-language validation, bias monitoring, privacy protection, clear escalation and qualified clinical judgement are required before and during any deployment.
Clinician reviewed
No autonomous diagnosis. Signals are interpreted with an interview, validated tools and the patient's complete clinical context.
Patient consent first
Voice analysis must be transparent, purpose-limited and optional, with clear controls over capture, access and retention.
Bias actively tested
Performance must be evaluated across languages, dialects, ages, genders, devices and clinical settings before deployment.
Hear earlier. Care sooner. Prevent the next gap.
Make mental wellbeing visible within the everyday care relationship—without reducing a person to a score or creating another disconnected tool.