IndRAH builds autonomous patient workflows that capture clinical signals with precision, from the words a patient struggles to find to everything a provider needs to convey in the course of care.
It streamlines work like one of your own.
A patient interacts, in their own language and their own order, Aarambh listens past the hesitations to what actually matters. The patient receive constant support up until they enter the premises, while providing the staff and clinicians more time to focus on accuracy and efficiency.
The relationship that holds the connection.
It keeps the patient and their clinician updated, brings them back for what they need, and turns a single visit into a journey towards holistic care. The warmth of a provider that remembers you, working for every patient, every day.
Walk in ready to be understood.
Organise glaring thoughts and data from wearables. Anyone can talk through and share what is going to structure their concerns to be shared with any healthcare professional in minutes.
It is spoken under stress, complexed with varied clinical terms and medications that sound nothing like everyday speech, and rarely in just one language or pronunciation. We build for that reality from the ground up, so what reaches the record is what the person actually meant, wherever the conversation happens.
Understands code-mixed speech, and our adaptive vocabulary to make patients more comfortable.
Over a phone call, WhatsApp, or an app. IndRAH meets people where they feel safe.
IndRAH optimises patient workflows to work with your existing team and pipelines, not the other way around.
The field of healthcare and clinical data capture is an ever-evolving research problem, so we treat our agents the same.
The core challenge of autonomous agents for clinical conversations is that the user's who interface with them are generally limited by atleast one sensory modality or lack physiologic state due to a pathology or distress. We constantly strive to share our findings and data on improving voice agents for healthcare.
When speech-to-text mishears a medicine, the record breaks. Our first public dataset maps exactly how, for Indian multilingual speech.
We'll help you find the right solution that suits your needs.
