Launching today
ChikitAI
Agentic AI for Healthcare Triage and Care Automation
25 followers
Agentic AI for Healthcare Triage and Care Automation
25 followers
ChikitAI is the healthcare agentic AI by NyuktAI that automates healthcare's messiest bottleneck: patient intake and triage, increasing patient intake capacity by 30% for healthcare providers. It talks to patients in natural language, captures a clinical-grade history, assesses urgency, and routes them to the right care. Built on proprietary clinical LLMs, it cuts wait times, reduces no-shows, and gives clinicians their time back.
ChikitAI
Refocus
The detail that stands out is routing on urgency rather than just symptom keywords. I build voice AI that checks in on aging parents by phone, and the toughest cases are the ones where someone underplays how they actually feel, so a confident triage decision can be exactly wrong. Does ChikitAI expose an uncertainty signal and hand borderline cases to a human, or does every intake get a definitive routing? Also curious how you validated the clinical LLM against real intake transcripts.
ChikitAI
@igorgurovich Thanks Igor. Voice AI is need of the hour. Great to know, you have built and tested it out. We have integrated external Voice AI agents for this. Happy to learn more about your Voice AI trained on clinical workflows.
On uncertainty: ChikitAI never issues a definitive routing. That was a deliberate architectural choice. Every assessment outputs a probability distribution across differentials with confidence scores. e.g. gastroenteritis 65% / appendicitis 20% / other 15%, not a verdict. That distribution goes to a clinician alongside the structured history, red flags, and the reasoning trace behind it. The doctor decides. We're a decision support layer at the first mile, not an autonomous router. In regulated care, a confident wrong answer is worse than a useful uncertain one, so we optimised for surfacing uncertainty rather than hiding it behind a single output.
Red-flag escalation runs as a separate rule-based path on top of the probabilistic layer. chest pain, breathing difficulty and similar trigger escalation regardless of what the model's confidence says. Probabilistic reasoning is good at the middle of the distribution and bad at the tail; deterministic rules cover the tail.
Genuinely curious how you're handling this on the voice side, voice gives you prosody and hesitation, which is signal we don't have in text. Does that actually help you catch the underplayers, or is it noisier than it looks?
One thing I'd love to see is multilingual support built in from the start, not as an afterthought. Healthcare serves incredibly diverse communities, and having intake conversations in a patient's preferred language right out of the box would make a real difference for accessibility and trust, especially for elderly or non-native English speakers who often struggle with current intake forms.
ChikitAI
@lknursafalge6s Thanks for the comment IIknur. You are spot on with identifying the language barrier at the very entry point of care. Forms, explaining the symptoms with their respective cultural context are major barriers for the physicians to get a full picture of the underlying case. Thats why Multilingual isn't a layer we added on top. It's where we started. The reason is that most systems treat language as a translation problem. Patient speaks, text gets converted to English, then the clinical model reasons over the English. But meaning leaks at that seam. People describe pain, duration, and severity through idiom that doesn't survive translation. By the time the model sees it, the nuance is already gone, and the model is reasoning confidently over a degraded input. So our clinical LLMs convert the patient's own language directly into medical terminology, no English hop in the middle. The reasoning happens on what they actually said. Voice input is part of this too, for anyone who finds forms hard for reasons that have nothing to do with language.
Curious which languages or communities you had in mind? Genuinely useful input for how we prioritise.