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Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.
Abridge’s original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.
The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.
Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint’s Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation.
We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.
We discuss:
Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week
The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives
Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)
Chai’s “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout
Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters
The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room
Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat
How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR
The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma
The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting
When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters
Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents
How Abridge approaches personalization across individual doctors, specialties, and health systems
Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel
Abridge’s eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout
HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely
What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization
Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows
How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption
Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward
Why Abridge embeds “clinician scientists” into product and eval teams
What Chai learned from Glean about search, quality, and durable AI infrastructure
Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans
Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products
How Abridge uses Claude Code, Cursor, and coding agents internally
Abridge:
Website: https://www.abridge.com/
X: https://x.com/AbridgeHQ
Janie Lee:
LinkedIn: https://www.linkedin.com/in/janiejlee
Chaitanya “Chai” Asawa:
LinkedIn: https://www.linkedin.com/in/casawa
Timestamps00:00:00 Introduction and what Abridge does
00:02:05 From ambient documentation to clinical intelligence
00:04:04 Clinical decision support and context as king
00:06:57 Alert fatigue, proactive intelligence, and prior authorization
00:12:36 Ambient AI form factors and healthcare customers
00:16:59 The hardest AI problems in healthcare
00:18:26 Frontier models, proprietary data, and model strategy
00:21:07 The EHR as a filesystem for agents
00:24:03 Personalization, memory, and clinician preferences
00:30:40 Evals, LLM judges, and progressive rollout
00:36:47 HIPAA, de-identification, and privacy
00:39:21 100M conversations and operating at scale
00:44:10 EHR integration and the clinical intelligence layer
00:46:39 Healthcare regulation, latency, and high-stakes AI
00:50:11 Clinician scientists and long-tail quality
00:53:04 Lessons from Glean and durable AI infrastructure
00:57:03 The future of agentic healthcare workflows
00:57:34 PRDs, product clarity, and building serious AI products
01:03:11 AI coding tools at Abridge
01:04:06 Outro
TranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.
Jacob [00:00:07]: Very excited to do this.
Jacob [00:00:08]: At this point, we get together once a year.
Swyx [00:00:10]: Once a year
Jacob [00:00:11]: And this is a fun occasion to get to do it on.
Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint’s our big investors and supporters of Abridge.
Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcast
Jacob [00:00:29]: Please, by all means.
Swyx [00:00:31]: So we’ll introduce our guests. Chai and Janie, welcome to the pod.
Janie [00:00:34]: Thanks for having us.
Chai [00:00:35]: Thank you.
Janie [00:00:35]: We’re excited to be here.
Chai [00:00:36]: Thank you.
Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?
Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they’re spending 10 to 20 hours a week on documentation. There’s a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It’s where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it’s the claim, the payment, the actual diagnosis given, the treatment. And we’ve started with a conversation to reduce the burden for doctors on documentation but we’re really excited about the path ahead as we become this broader clinical intelligence layer.
Chai [00:01:34]: I’m Chai. I work on clinical decision support at Abridge.
Swyx [00:01:37]: Yes.
Chai [00:01:37]: And so as Janie said, we’re uniquely situated where we started off with the clinical note. What I’m really excited about and where we’re expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.
Swyx [00:02:01]: And that’s the context engine that you guys have?
Chai [00:02:04]: Yes.
Swyx [00:02:04]: Is that what it’s called? Okay.
Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there’s been a big transition in the company. Tell me about the broader transition.
From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that’s where a lot of that original product was.
Swyx [00:02:37]: By the way, one of those interesting stats
Swyx [00:02:39]: On your landing page was, doctors spend time after hours.
Janie [00:02:43]: They call it pajama time.
Swyx [00:02:44]: Why is that pajama time?
Janie [00:02:46]: Doctors after work in their pajamas
Swyx [00:02:48]: In their pajamas. Oh
Janie [00:02:49]: At home are just writing and catching up on their notes every day.
Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we’re now finally able to
Janie [00:03:06]: go home and eat dinner with our kids for the first time.”
Chai [00:03:08]: Save the marriage in some cases.
Swyx [00:03:10]: One of the quotes was “We’re not divorcing anymore.”
Swyx [00:03:12]: I’m asking, “Why?”
Swyx [00:03:14]: Because they’re working too much.
Janie [00:03:16]: But, in terms of where we’re going and where we’re expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It’s getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.
From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that’s interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It’s really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I’m sure a lot of our listeners are curious what’s similar about the problems that you’re going after now and what feels different, now that you’re in healthcare.
Chai [00:04:33]: Very similar. Taking a step back, with every wave, there’s a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we’re seeing that very similar in the agent era with many companies, of course, in Redpoint’s portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we’re in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it’s “Oh, you got the question wrong.” It wasn’t the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there’s a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there’s a large variance but when Glean is, it’s a much more horizontal company, there’s a variance of personas, companies that you’re working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you’re able to focus far more, especially when you have a maturing technology and you’re building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it’s extremely ripe for AI to keep helping augment and enable. And the final thing that’s really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we’re ambient and we’re always listening in the background. And many more AI products will go that way but it’s how we started. And that’s the greatest form of AI we can create, AI that’s seamless. You’re not looking at your screen. It’s always there. It’s always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.
Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there’s been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It’s probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?
Janie [00:07:26]: It’s such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we’re acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they’re with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they’ve had over the course of months or years and they’ll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We’ll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you’re going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it’s really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor’s office with knee pain. They’ll prescribe you an MRI and so many of us have had this experience before, where in four weeks you’ll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn’t approved and why don’t you come back in? We’ll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he’s on in California requires six things. We’ve already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.
Prior Authorization: Reducing Latency in CareChai [00:10:23]: There’s this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They’re going to get ignored if you get alerts that... Similarly in engineering, where they’re noisy alerts that you can’t act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.
Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you’re on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.
Jacob [00:11:31]: I thought this episode was
Jacob [00:11:31]: To make sure we didn’t scare people from healthcare.
Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.
Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I’d say the last is workflo