Nabla

Nabla is an ambient AI medical documentation platform founded in Paris, France in 2018 by Alexandre Lebrun, who previously built Wit.ai (acquired by Facebook) and led Facebook's AI Research lab (FAIR), alongside co-founders Delphine Groll and Martin Raison. The product launched its current ambient scribing form in March 2023 and has since grown to serve 85,000+ clinicians across 150+ health organizations in the United States and Europe, including CVS Health, Accolade, and Children's Hospital Los Angeles. The core problem it solves is physician documentation burden: clinicians in the US spend an average of two hours on EHR tasks for every hour of direct patient care, and Nabla's ambient AI cuts that overhead substantially by transcribing and structuring encounters automatically.

The platform works across a smartphone app (iOS and Android), a Chrome browser extension, and a web interface. During an encounter, Nabla listens passively, distinguishes clinician and patient voices, filters out non-clinical conversation, and generates a structured clinical note in under 10 seconds using proprietary speech-to-text and a fine-tuned medical LLM. Notes are exported directly into 20+ EHR systems including Epic, athenahealth, Oracle Health, NextGen, and Greenway Health. Beyond scribing, Nabla has expanded into proactive coding (ICD-10, HCC, and CPT suggestions), automated referral letters, and post-visit patient-facing summaries. The platform supports 50+ medical specialties and more than 35 languages, including Arabic, Mandarin, Vietnamese, French, and Spanish, and is HIPAA-compliant, GDPR-certified, SOC 2 Type 2, and ISO 27001 audited.

What Nabla actually is in May 2026

Nabla began as a consumer health app and pivoted entirely to clinical ambient AI documentation in 2023. By May 2026, it has grown into a platform that handles the full documentation workflow for an encounter: live transcription, note generation, EHR push, medical coding suggestions, referral letters, and patient summaries. The underlying architecture uses Nabla's own proprietary speech-to-text (fine-tuned on medical data) rather than general-purpose transcription APIs, which accounts for the accuracy it demonstrates in high-noise clinical environments and across accented speech.

In July 2024, Nabla rolled out system-wide across Carle Health's 1,500 Illinois-based providers through a native Epic integration. The deployment followed a four-week pilot across family medicine, pediatrics, and cardiology, in which over 50% of participating clinicians reported saving at least one hour of documentation time per day. Dr. David Lovinger, Associate CMO and CIO of Carle Health, stated: "Nabla fits seamlessly into our workflows, and thanks to the onboarding support and training we received throughout the pilot program, we started seeing results immediately. Nabla enhanced the patient experience at our pilot sites by giving our providers more face time with their patients, knowing that it was doing the heavy lifting for charting."

In June 2025, Nabla raised a $70M Series C led by HV Capital and Highland Europe, joined by DST Global and existing investors Cathay Innovation and Tony Fadell's Build Collective, bringing total funding to $120M. The round followed five-times revenue growth in the prior six months and signaled the company's expansion into agentic workflows that go beyond note generation into automated clinical task execution.

Where Nabla sits versus Abridge and Suki AI

The ambient AI scribe market has three credible enterprise players alongside Nabla: Abridge, Suki AI, and Microsoft Nuance's DAX Copilot. The mechanical differences matter when choosing.

Abridge built its own proprietary speech recognition and LLM models from the ground up, meaning it is not a fine-tuned wrapper around any third-party foundation model. More importantly, Abridge holds an official partnership with Epic that makes it the native AI scribe surfaced inside Epic's own interface at major health systems including Mayo Clinic, Kaiser Permanente, and Duke. That structural advantage gives Abridge a 3-6 month integration depth lead over any competitor on Epic-native workflows. Abridge also generates post-visit patient-friendly summaries as a default output alongside the clinical note, which Nabla offers only as an add-on. The tradeoff: Abridge targets large enterprise deployments at roughly $2,500 per user per year, is US-centric, and operates primarily in English. Nabla's 35-language support and EU-compliant infrastructure have no Abridge equivalent. Clinicians at non-Epic-centric health systems, international organizations, or smaller practices will not benefit from Abridge's core advantage.

Suki AI takes a fundamentally different architectural approach: it is a voice-command clinical assistant layered on top of EHR workflows, not a passive ambient listener. Clinicians say "Suki, show me the latest labs" or "Suki, add a prescription for metformin" to interact with the EHR by voice. The passive scribing capability is present but secondary to the command-and-control interface. Suki's pricing is estimated around $299 per clinician per month, roughly three times Nabla's individual rate. Recent user feedback from 2024-2025 reports reliability concerns: unexpected text behavior, declining dictation accuracy under heavy daily use, and workflow disruptions that undermine confidence in live documentation. Nabla's passive listen-and-generate model avoids command-mode friction, making it more suitable for clinicians who want to stay focused on the patient rather than managing a voice assistant. See also Hippocratic AI for AI agents focused on patient-facing clinical conversations rather than physician documentation.

How the AI actually works inside Nabla

Nabla's AI stack has two main components: a proprietary speech-to-text layer fine-tuned on medical vocabulary and accented speech, and a downstream LLM trained to extract clinically relevant content and structure it into note templates. Grégoire Retourné, Nabla's ML lead, explained the distinction in a January 2024 interview: "When you have just a normal speech-to-text algorithm, they may or may not be good on medical data. But we have a fine-tuned one." This is what separates Nabla from tools built as thin wrappers around OpenAI Whisper or Google Speech-to-Text in general-purpose form.

The LLM does three things: it filters non-clinical conversation (small talk, pauses, off-topic exchanges), it identifies the structure of a clinical encounter (chief complaint, history, physical exam, assessment, plan), and it populates a note template in the clinician's preferred style. Customization is available via custom instructions and dot phrases. The platform retains no audio by default after note generation, which addresses one of the primary privacy concerns clinicians raise about any ambient listening product.

The proactive coding agent analyzes the completed note and suggests ICD-10, HCC risk adjustment codes, and CPT billing codes, reducing the documentation-to-billing gap that costs medical practices revenue. For evidence-based clinical guidance, clinicians pair Nabla with OpenEvidence, which handles clinical Q&A that Nabla does not offer.

"With Nabla, I am finally getting my weekends back. What took 8-10 hours now takes maybe 3-4 hours. Better yet, because I no longer need to be a scribe, I get to actually be a psychologist and focus on my patient. After only a couple months, I can not imagine my practice without Nabla." - Dr. Mischa Antin Tursich, Psychologist, nabla.com/testimonials, January 2024

A randomized clinical trial published in NEJM AI (2025) measured Nabla against a control group across 72,000 patient encounters. Nabla users showed a 9.5% decrease in time-in-note, with improvements in physician burnout scores (Mini-Z scale: +2.69), task load reduction, and work exhaustion (PFI-WE: -0.23). The trial's authors noted that "active physician oversight" remains essential, as the tool occasionally produced clinically significant inaccuracies. That qualification is important: Nabla generates draft notes, not final ones.

The friction and accuracy concerns users keep raising

Nabla's testimonials skew strongly positive, which is expected from a vendor-curated page. Forum discussions on Reddit's r/Psychiatry and r/medicine tell a more textured story.

The hallucination risk is real and documented. The NEJM AI trial reported occasional clinically significant inaccuracies. Nabla's own marketing acknowledges a 5% note-adjustment rate, which translates to one note in twenty needing substantive correction. In high-volume emergency medicine or psychiatry, one corrected note per twenty encounters is manageable; in complex multi-system encounters, it demands attention.

Specialty suitability varies. Psychiatry users frequently note that the History of Present Illness section generated by Nabla for complex psychiatric evaluations is weaker than competing tools like Freed. Auto-generated mental status exams often require rewriting for nuance that the ambient transcription layer cannot reliably infer from spoken conversation alone.

The free tier creates a compliance trap. Nabla's free plan does not include a HIPAA Business Associate Agreement. Clinicians running HIPAA-covered encounters on the free plan are technically out of compliance. This gets mentioned regularly in Reddit threads as a reason to either upgrade immediately or avoid the free tier entirely for clinical use. The paid plan resolves this, but it means the trial-to-paid conversion is effectively mandatory for legal clinical deployment.

Longitudinal patient context is limited. Each encounter is treated largely in isolation. Nabla does not pull prior-visit notes to inform the current note's structure or catch inconsistencies between visits. For practices managing patients with complex chronic conditions, this means the AI cannot flag that today's reported symptom contradicts something documented three visits ago. The company has listed longitudinal context as a roadmap item, but as of May 2026 it remains underdeveloped.

Custom prompt character limits constrain power users. Clinicians who want to specify detailed note formatting rules, specialty-specific language, or complex documentation instructions sometimes find the prompt field truncated, which limits how far individual customization can go without enterprise-tier support.

"I've got four users on Nabla, and each one of them have come into my room saying how brilliant it is. I've never had such a positive response about anything ever. We believe the greatest benefit is in making doctors enjoy their work more and spend less time editing notes for referral letters, among other things." - Tom James, GP Director, nabla.com/testimonials, September 2023

Who Nabla is for

Nabla fits best for solo clinicians and small group practices wanting enterprise-grade ambient scribing at an accessible individual price (around $99 per month), multilingual practices and European health organizations where GDPR compliance and 35-language support are hard requirements, and high-volume specialties where documentation load drives burnout: primary care, emergency medicine, psychiatry, and behavioral health. Health systems or EHR vendors that want ambient scribing infrastructure via API are also a natural fit; Nabla powers white-label solutions inside NextGen Ambient Assist and Greenway.

Skip Nabla when your organization runs entirely on Epic and wants the deepest possible native integration: Abridge's structural Epic partnership holds a real advantage there. Skip it if clinicians need AI-generated differential diagnoses or clinical decision support: Nabla is documentation-only, and tools like Glass Health combine scribing with differential generation and clinical Q&A. Skip it if voice-command EHR control is the primary use case: Suki's architecture is purpose-built for that model. And skip the free tier for clinical use entirely; the missing BAA is a compliance gap that matters from day one.

For practices evaluating Abridge or Suki AI, the decision turns on deployment context: Nabla's multilingual capability, broader EHR coverage, lower individual pricing, and EU compliance infrastructure make it a stronger fit for non-Epic-first and international deployments. Clinicians who also want AI-assisted evidence retrieval often pair Nabla with OpenEvidence, which handles clinical Q&A that Nabla does not offer.