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TOGETHER WITH NABLA

Perspectives from the people building the future of health AI…

We sat down with Christina Boulier, VP of Customer Success at Nabla, to discuss what it takes to deploy ambient AI in real-world clinical settings. Nabla began with documentation, but is increasingly positioning itself as a broader clinical AI assistant, supporting coding, integrations, and workflow optimization across care environments.

Christina shared how Nabla approaches trust and transparency, why customer success plays a central role in enterprise AI adoption, and how the company is thinking about the transition from ambient AI to more adaptive, agentic clinical support.

Let’s start at a high level. How do you describe Nabla today?
At its core, Nabla helps clinicians generate high-quality clinical notes using ambient documentation and dictation. The original problem we set out to solve was the documentation burden that pulls clinicians away from patient care and contributes directly to burnout.

Today, documentation is still the foundation, but the product has grown beyond just generating notes. Nabla now supports clinicians with real-time assistance during the visit and downstream workflows like coding and documentation support. The note remains central, but it is increasingly part of a broader system that supports clinicians before, during, and after the encounter.

What problem were you most focused on solving when Nabla first launched?
Clinician burnout was the primary focus from the beginning. Documentation has been one of the most time-consuming and frustrating parts of care delivery, and it was clearly affecting clinician well-being.

From day one, trust was critical. If the note quality was not strong, or if clinicians felt they had to spend a lot of time editing output, the product wouldn't succeed. Accuracy, reliability, and the ability for clinicians to easily edit and customize notes were core priorities from the start.

Ambient documentation is now a crowded space. What differentiates Nabla?
One of the biggest differentiators is how closely we focus on workflow fit and clinician voice. The note has to sound like it was written by the clinician. That consistently comes up as a deciding factor when clinicians choose Nabla.

Another key differentiator is breadth. Nabla supports a wide range of languages, multiple specialties, and different care settings. That forces the product to be flexible and adaptable rather than optimized for a single narrow use case. Finally, customer success is a major part of differentiation. The technology matters, but adoption depends heavily on how the product is implemented, supported, and improved over time.

How does Nabla integrate into clinicians’ existing workflows and EHRs?
Integration is essential. Clinicians don’t want another tool that sits outside their core workflow. Nabla integrates deeply with major EHRs like Epic, Oracle Cerner and athenahealth so that documentation happens naturally within the tools clinicians already use.

The goal is to reduce friction as much as possible. Clinicians should be able to focus on the patient conversation while Nabla captures and structures documentation in the background, without requiring major workflow changes.

Customer success seems central to Nabla’s strategy. Why is that?
Because AI adoption in healthcare is rarely plug-and-play. Deployments involve IT teams, compliance, clinical leadership, and frontline clinicians, all of whom have different priorities and concerns.

Our customer success teams work closely with organizations from the start. That includes onboarding, specialty-specific training, and ongoing optimization. We spend time listening to clinicians, collecting feedback, and adjusting the product based on how it’s actually used. That ongoing feedback loop is essential for building trust and driving long-term adoption.

What does a successful rollout look like in practice?
We look at a few core outcomes: efficiency, clinician well-being, and patient experience.

For example, at Carle Health, 55% of clinicians saved at least one hour of documentation time during the pilot. At the University of Iowa Health Care, there was a 30% reduction in clinician burnout within the first 30 to 90 days. At Denver Health, Nabla contributed to a 15-point improvement in patient satisfaction scores and enabled 1.5 times more overbooked appointments per month.

Those kinds of results signal that the product is delivering meaningful value.

How do you build trust with clinicians who may be skeptical of AI?
Trust starts with transparency. We are HIPAA, GDPR, SOC 2 Type 2, and ISO 27001 certified, and we are very clear about how data is handled and protected.

Clinicians also trust other clinicians. We provide peer references, clinician testimonials, and peer-reviewed research. Nabla was the first ambient AI company to submit a model card through the Coalition for Health AI, which helps explain how models are trained and evaluated.

We also build trust by listening. When clinicians give feedback, we act on it quickly and visibly.

Can you share an example of how clinician feedback directly shaped the product?
A recent, quick win example came from a CMIO from one of our partner organizations. A clinical leader pointed out that the transcription timer only showed minutes, which could make clinicians worry the system was not actively transcribing during the first 59 seconds.

We updated and shipped a new recording screen within two weeks to show seconds as well as minutes, making it immediately clear that transcription was happening in real time. Small changes like that build trust and reinforce that clinician feedback matters..

Where is Nabla headed next?
We’re most excited about continuing to expand Nabla beyond the encounter note into a comprehensive clinical AI assistant that supports clinicians before, during, and after the visit.

In 2026, that means not only continuing to scale our ambient documentation and dictation experience, but also extending Nabla into the downstream workflows that drive efficiency and care quality, including more advanced coding support, deeper EHR integrations, and flexible, organization-specific configurations across teams and specialties. We’re also building new capabilities that continue to support care settings like inpatient as well as nursing.

To achieve our agentic AI vision, we’re partnering with Advanced Machine Intelligence (AMI Labs), Yann LeCun’s newest venture, to complement our proprietary LLM architecture with world models - a major step forward toward the reliable, autonomous agents clinicians can trust.

What stood out, what’s tricky, and why it matters…

Nabla sits in one of the most crowded parts of healthcare AI: ambient documentation. But after speaking with the team, it’s clear they see themselves less as a scribe vendor and more as a long-term workflow partner inside health systems.

The note is the starting point and the anchor. By capturing the full transcript and structured documentation inside the EHR, Nabla positions itself to extend into coding support, configuration across specialties, and broader clinical workflow assistance. The strategy is to embed deeply where clinicians already work, prove value, then expand outward from there.

With tens of thousands clinician users across hundreds organizations, and a recent $70 million Series C led by HV Capital bringing total funding to $120 million, Nabla has real enterprise traction behind it. The next chapter is about whether documentation can serve as the foundation for a durable clinical AI platform.

What stood out

What’s tricky

Final thoughts

Ambient documentation is quickly becoming table stakes. The real question is which companies can embed deeply enough into clinical workflows to expand beyond the note in a thoughtful and durable way. Nabla’s approach is fairly pragmatic: start with documentation, integrate tightly into the EHR, prove measurable value, build strong feedback loops, then extend into adjacent workflows with care.

The recent Series C funding and partnership with Yann LeCun’s new venture give the company additional momentum as it works toward that broader platform vision. The next phase will test whether documentation can realistically serve as the foundation for a larger clinical AI system.

From what we gathered, Nabla looks like a company setting itself up to secure a long-term seat inside the clinical workflow by combining product velocity with enterprise credibility and strong implementation muscle.