At Suki, we believe ambient AI will redefine how care is delivered. While others in healthcare tech chase the possibilities of generative AI, Suki is rigorously raising the bar and delivering a higher standard of note quality. By combining a deep understanding of user preferences, specialty-specific workflows, and visit-specific context, Suki ambient AI generates notes that feel personal, accurate, and truly reflective of your clinical voice. We recognize that high-quality notes are more than just documentation; they’re an extension of your care.
Suki’s multi-layered approach to documentation fuses cutting-edge AI with deep clinical oversight, pairing a powerful tech stack with a dedicated Clinical Operations team to ensure every note meets the highest standards of accuracy, consistency, and usability. The result is transformative: we’re not just generating notes — we’re crafting them to accurately align with your speciality. By prioritizing trust, clinical nuance, and precision, we’re delivering industry-leading note quality that is accurate, evidence-based, specialty-specific, and ready for real-world use.
Building AI with Clinical Integrity at Its Core
At the heart of Suki’s success is our proprietary Automated Speech Recognition (ASR) engine that has been trained on millions of hours of medical conversations. For more than eight years, we have been training and refining Suki’s engine, which, unlike general-purpose ASRs, is built specifically for healthcare. This specialization enables it to recognize complex medical terminology, adjust quickly to new medications and procedures, handle conversations in multiple languages, and capture natural clinician speech with remarkable fidelity, even in a noisy clinical setting.
But the ASR engine is just the first layer.
Suki’s system also features an LLM Manager—a form of orchestration that allows Suki to dynamically select the best large language model for different tasks, whether it’s summarizing a patient history or generating an assessment and plan. Rather than rely on a single LLM provider, Suki draws from a portfolio of models from Google Gemini and OpenAI, to ensure consistent quality and minimize disruption from outages or performance drops. Suki regularly evaluates the latest models and model versions in the market to choose the one that generates notes with the best quality.
Suki also employs an evidence-based linking model to counteract the industry-wide problem of “AI hallucinations,”. By grounding every sentence in either the EHR or the encounter transcript, clinicians can trace the origin of any note content, building auditability, transparency, and trust directly into the product. The result is improved note quality, organization, and clear audit trails clinicians can trust.
Precision That Speaks Every Specialty’s Language
One of the biggest challenges for AI documentation has been scaling effectively beyond primary care and into a variety of specialties. Suki has made specialty performance a priority.
Its system uses key provider and patient context not always captured in the spoken encounter to tailor AI content. Suki has a large network of clinicians across specialties that generate high-quality clinical note examples known as “gold notes”, which are used as a gold standard for clinical documentation for our language models. This network also performs rigorous evaluations of its specialty-specific notes to ensure clinical accuracy, usefulness, and appropriateness for the specialty. This ensures that an ENT or behavioral health provider receives output aligned with the structure, terminology, and clinical nuances of their specific field.
A Feedback Loop of Human + AI Oversight
At Suki, AI quality has always been a high priority, which is why we are continuously driving for even higher standards and the best possible outputs. The company operates a dedicated Clinical Operations team that works in tandem with proprietary AI agents to constantly evaluate, assess, and improve clinical notes. These notes are evaluated using a modern version of the PDQI-9 (Physician Documentation Quality Instrument) updated for AI, a clinically validated framework that scores documentation quality and guides refinement across models.
This hybrid model of machine learning and human-in-the-loop evaluation allows Suki to respond quickly to feedback, correct errors, and scale improvements across specialties and systems.
Quality That Drives Outcomes
The impact is measurable. Today, 97% of Suki’s ambient notes are linked to supporting evidence from transcripts or the EHR. With the help of a vast clinician-led specialty network, advancements in bleeding-edge technology, including AI agents, and a medically-tuned ASR, the system has been strategically refined to produce cleaner, more personalized AI content with enhanced audit trails. These enhancements ensure greater accuracy, consistency, and trust, starting with each note that is generated.
The Broader Implication: AI That Earns Clinician Trust
Suki’s approach represents a meaningful shift in elevating clinical documentation. By focusing on accuracy, transparency, and clinical relevance, Suki is redefining what AI-generated notes can and should be.
In a healthcare landscape where AI solutions often overpromise and underdeliver, Suki stands apart by earning clinician trust, one high-quality note at a time.
