Suki AI

Suki is a voice-first AI clinical assistant designed specifically for physicians and healthcare organizations. Founded in 2017 by Punit Soni (formerly VP at Flipkart, with earlier roles at Google and Motorola) and headquartered in Redwood City, California, Suki addresses one of medicine's most persistent problems: the documentation burden that keeps clinicians at their keyboards instead of with their patients. By October 2024, the company had secured $165 million in total funding, including a $70 million Series D led by Hedosophia, and had grown 4x in a single year to serve more than 400 healthcare systems across the United States.

The product comes in two configurations. Suki for Clinicians (sold as Suki Assistant) is the direct-to-physician app available on iOS, Android, and web: it listens to patient encounters, generates structured clinical notes, stages prescription and lab orders from ambient conversation, surfaces relevant EHR chart data by voice command, and suggests ICD-10 and HCC billing codes. Suki Platform is a developer API and SDK that healthcare technology companies embed into their own products, using Suki's underlying ambient and voice engine without building the infrastructure themselves. Both paths integrate natively with Epic, Oracle Health, athenahealth, MEDITECH Expanse, and Azalea Health, across 100+ medical specialties and 80+ languages.

What Suki AI actually is in May 2026

Suki started life as a smart dictation assistant, and that voice-first heritage shapes everything about the current product in ways that distinguish it from ambient-only competitors. When a clinician opens Suki on their iPhone and walks into an exam room, they can trigger ambient listening mode with a single tap. From that point, Suki listens, transcribes, and begins structuring a note in real time. After the encounter, the physician reviews the draft, makes edits using voice commands ("Suki, move the medication to the assessment section"), and pushes the note into the EHR with one confirmation.

But Suki is not just a transcription pipe. The voice command layer extends to clinical workflow: physicians can say "Suki, stage a metformin prescription for 500mg twice daily" or "Suki, pull up the last HbA1c result" during the encounter itself, and the system queues those actions against the connected EHR. This hands-free EHR control is the feature that most distinguishes Suki from tools like Nabla or Abridge, which focus primarily on ambient note generation rather than voice-driven workflow automation.

As of mid-2026, Suki's core benchmarks stand at: average 72% reduction in time spent on documentation per note, 41% faster note completion, and a documented reduction in clinician burnout of 63% per Peterson Health Technology Institute data. KLAS Research validated Suki with a 93.2 out of 100 performance score, with 95% of surveyed organizations stating they would buy again. The Apple App Store rating sits at 4.2 out of 5 stars across 63 reviews, consistent with KLAS findings but reflecting some real-world friction (discussed in the frustrations section below).

"Before Suki, I was tied to my computer. Now I can be fully present with my patients. That makes all the difference." - Dr. Melissa Holmes, Pediatrician, suki.ai, 2026

Where Suki sits versus Abridge and Nabla

Abridge is Suki's most direct enterprise competitor in the US. Abridge raised $150 million in Series C funding in early 2024 (led by Lightspeed with NVIDIA participation) and by June 2025 had reached a $5.3 billion valuation. The mechanical difference is distribution architecture: Abridge gave Epic an equity stake and ongoing revenue share to secure preferential placement inside Epic's third-party vendor program. This keeps Abridge 3 to 6 months ahead of rivals on integration depth for the 38% of US hospitals that run Epic, and explains its deployment at Kaiser Permanente (24,600 physicians), Mayo Clinic (2,000+ physicians), Duke Health, and UPMC. For organizations already deep in Epic, Abridge has a structural advantage. For health systems on MEDITECH, Oracle, or athenahealth, Suki is often the stronger choice. Suki's mobile-first design also makes it more practical for hospitalists and other specialties that move between rooms rather than sitting at a fixed workstation. You can compare Abridge directly on its Vantaige listing.

Nabla approaches the market from the opposite direction. Founded in Paris, Nabla built GDPR compliance into its architecture from day one, making it the default choice for European health systems where US-centric tools face regulatory friction. Nabla raised $24 million in Series B (January 2024) and $70 million in Series C (June 2025, led by HV Capital), bringing total funding to roughly $120 million. It supports 35+ languages, integrates with all major EHRs through standards-based APIs, and spent three years building proprietary language models before layering in third-party LLMs. The generated note appears in under 20 seconds. Where Nabla differs most: it does not offer deep voice command control over EHR workflows, and its ambient-only approach is closer to Suki's pre-2021 product than to Suki's current voice-command assistant. Nabla is strong for European buyers and multilingual practices that prioritize GDPR-native architecture over voice-driven workflow control. Practices wanting AI beyond documentation should also look at Glass Health for clinical reasoning and OpenEvidence for evidence-based research during encounters.

How AI actually works inside Suki

Suki's AI pipeline is not a single model. Ambient listening uses a medical-grade speech recognition layer tuned on clinical audio, not general-purpose transcription. That transcript feeds into a structuring model that identifies note sections (History of Present Illness, Assessment and Plan, medications, orders) and populates them using the encounter context. A separate coding model cross-references the structured note against ICD-10 and HCC hierarchies to flag billing opportunities or documentation gaps. On top of all of this, the voice command parser handles real-time directives, distinguishing clinical dictation ("the patient reports chest pain radiating to the left arm") from system commands ("Suki, start a new section").

The Suki Platform product exposes much of this pipeline as API endpoints, allowing healthtech companies to build ambient documentation, voice command handling, and coding assistance into their own products without rebuilding the infrastructure. This B2B SDK model has attracted partners including Zoom (ambient note generation for its 140,000 healthcare organization customers, announced January 2025) and a deepened MEDITECH partnership (announced July 2025).

Regarding EHR integration depth: Suki does not use simple copy-paste or clipboard injection. Native integration means notes write back into the EHR's structured fields, orders queue in the EHR's order management system, and chart data retrieval pulls from live EHR APIs. The July 2025 MEDITECH Expanse integration was notable because Suki became the first ambient AI vendor to send ambient notes directly into MEDITECH's documentation APIs without a manual copy step. As of that milestone, over 100,000 patient encounters had been processed and more than 1,000 providers were active on MEDITECH Expanse alone.

"With Suki ambient documentation, I can pull the pieces and parts from ambient as well as maintain the other pieces I use in Epic." - Dr. Bobby Dupre, CMIO, Franciscan Missionaries of Our Lady Health System, Suki 2024 Year in Review

The friction and cost concerns physicians keep raising

The most recurring complaint is straightforward: price. At $299 per clinician per month for Suki Compose and $399 per clinician per month for Suki Assistant, Suki costs 2 to 3 times more than alternatives like Freed AI ($119/month) or Heidi Health ($90-99/month). For solo practitioners and small independent practices, the per-clinician cost rarely survives a budget conversation. Suki's business model is built for enterprise health systems where IT, compliance, and onboarding costs are amortized across hundreds of providers, not for a three-physician family medicine practice.

The second recurring issue is note quality over time. Several physician reviewers note that after extended use, Suki's generated notes become formulaic and repetitive in phrasing, requiring more editing than during initial weeks of use. This is partly a consequence of how the structuring models work: they optimize for consistency and completeness, which can produce notes that read identically across similar encounter types. Physicians in high-volume specialties with diverse case mix report less of this issue than those handling routine chronic disease management at high volume.

Voice command adoption adds onboarding friction. The voice command library is powerful but requires physicians to learn command syntax ("Suki, create an HPI for.."), and during the first few weeks the interruption pattern can slow encounters rather than accelerate them. Most practices report a 2 to 4 week ramp before the system feels invisible.

Drug recognition has a documented gap: the system occasionally fails to capture a medication mentioned conversationally or places it in an incorrect note section. Physicians practicing in pharmacology-heavy specialties (oncology, psychiatry, complex polypharmacy) report higher rates of medication-related edits than general internists. EHR coverage is also narrower than some competitors claim. While Epic, Oracle, athenahealth, MEDITECH, and Azalea are solid integrations, practices on eClinicalWorks, Kareo, ModMed, or other mid-market EHRs will find limited or no native support.

Who Suki is for, and when to pick something else

Suki fits enterprise US health systems that need multi-specialty, multilingual ambient documentation with real voice-driven EHR workflow control, and that have the IT infrastructure to support a full integration rollout. Ambulatory specialties with high encounter volume (primary care, cardiology, hospitalists, OB/GYN, gastroenterology) are the sweet spot. Organizations already running Epic, Oracle Health, athenahealth, or MEDITECH get the deepest integration. Health systems interested in embedding Suki's AI engine into proprietary apps should evaluate the Suki Platform API, which is documented and actively supported.

For related clinical AI tools, Hippocratic AI covers patient outreach and care navigation (a complementary rather than overlapping use case), while Glass Health focuses on clinical reasoning and differential diagnosis during the encounter itself.

Skip Suki when: you are a solo practitioner or small clinic where $399 per clinician per month is not justifiable by ROI; your practice runs an EHR outside Suki's integration list; you need features like AI receptionist, fax management, or integrated billing support in the same platform; or you want month-to-month flexibility and transparent self-serve pricing without going through an enterprise sales process. For those scenarios, lower-cost ambient scribes like Freed AI or Heidi Health are worth evaluating first, even if they lack Suki's command depth.