Launching today
An ambient AI scribe that writes clinical notes in real time so you can focus on your patient. It passively captures every visit, whether in person or virtual, and delivers a complete, structured clinical note the moment the encounter ends. HIPAA compliant, desktop native, and EHR ready in seconds. We're launching Scribe today, with a much bigger vision ahead: a medical intelligence platform that goes beyond documentation to support the entire clinical workflow.
ClinicFrame
Hi Product Hunt 👋 Clemente here, I lead ClinicFrame.
Three years ago we launched CompliantChatGPT, a HIPAA-compliant AI assistant for clinicians. More than 8,000 have used it since: therapists, psychologists, oncologists, nurses running home visits. They weren't shopping for AI. They came because they were already putting patient information into a consumer chatbot, with no BAA, no audit trail, and no answer for the surveyor who asks where that data went.
After three years of listening to them, the pattern was hard to miss. Almost every conversation ended up at the same place: the note. And most clinicians were writing each one twice, first as scribbles during the visit, then properly, hours later. Notes were never the job. So we built the tool that writes them.
🩺 What ClinicFrame Scribe does
It listens to the visit, in person or virtual, and writes the structured clinical note as it happens. Desktop native, so nothing joins your call as a third participant. SOAP by default, your own templates on top. You review it, you sign it, you stay the author, and it lands in your EHR in seconds.
🔒 HIPAA compliance is the floor, not the upsell.
A BAA comes with the account at self-serve price. Documenting a visit safely shouldn't require a procurement cycle and an enterprise contract, which is exactly what the big model providers ask for today.
🧭 Today it's a scribe. That's the starting point, not the plan.
We're building the operating system for a clinical practice: the context of every patient, the record of every visit, and the part nobody volunteers for, dealing with insurers. The note is where that starts, because the note is where the billing code comes from. Revenue is what we build next, with the clinician reviewing every code before anything leaves the building.
Most software in a clinic was built for the billing department and handed to the clinician afterwards. We're building it the other way around.
🧱 Every Ai medical tool on the market started as a scribe. We started three years earlier, on the compliance side, with 8,000 clinicians running real clinical work through a product that had to hold up to a BAA from day one. That's the harder half of the problem, and it's already done.
👥 Who it's for: clinicians who document their own visits. Therapists, private practices, home health agencies, small clinics.
🎁 For the PH community: 50% off for 3 months, on top of the 7-day free trial. No card to start, and onboarding runs a demo visit so you get your first note without waiting for a real patient.
🙏 Our ask: if you document patient visits, what's the one thing you'd never let a tool do with a session? That's the answer I care about most. I'll be in the comments all day.
@chiara_tucci @macarena_balparda @alan_brande @dana_fridman are here with me today, and thanks to @mishaal_rashid and @francesco_domizio for the hunt.
@chiara_tucci @macarena_balparda @alan_brande @dana_fridman @mishaal_rashid @francesco_domizio @clemente_lopez1 Congrats on the launch, guys.
I'm curious, are clinicians excited about real-time notes?
ClinicFrame
@dmitrii_volosatov
They trully are!
It is well known that a clinician spend 2 hours on administrative work per hour with a patient, it's a nightmare for them.
to answer the question you actually asked - pick a billing code on its own. everything else in the note is a record of what happened, but the code is a claim submitted to a payer, and the liability for it sits with the clinician, not the tool. you mention the clinician reviews every code before it leaves the building, which is the right instinct, but I'd push on how that review is presented. if the suggested code is shown as "the note says X, therefore code Y" it's very easy to rubber-stamp under time pressure, same failure mode as any confident-sounding suggestion. does the review step force the clinician to see the specific line in the note the code was derived from, or just approve the code itself
ClinicFrame
@galdayan
Right, the code is a different object. The note records what happened. The claim asserts it to a payer, and the clinician signs for it.
So the code never appears on its own. It appears with the exact line in the note it came from, and with whatever is still missing for it to hold. You approve the evidence, not the suggestion.
And when the visit doesn't support a code cleanly, we don't offer one. We say what's missing. Fewer suggestions, each one traceable, is the only version of this that survives an audit.
And after that comes the part where the insurance company denies the claim and we have to file an appeal—and here’s the real magic of it all: if we have the context, the evidence, and the reasons, we can automatically generate the appeal letter for the insurance company.
And of course, everything related to RCM—collecting a percentage of the transaction amount.
I'm not in healthcare, but the idea really got me. Really, congrats on the execution. The line about clinicians writing every note twice gets to the real problem. The transcription is useful, but cutting out that second round of admin is where the value is. I’d be interested in how ClinicFrame handles situations where the conversation is incomplete, contradictory, or clinically sensitive. Does it clearly flag uncertainty and missing details, or can a polished note make the output look more certain than the visit actually was? Keeping the clinician as the author is the right approach. The review step needs to be genuinely quick without encouraging people to skim.
ClinicFrame
@os_ishmael
Hey! Thanks for your comment, and you're absolutely right!
1. We point out any parts that weren’t fully understood to avoid errors.
2. Those comments can be edited, both in the transcript and directly in the note.
3. Once everything is ready, it’s ready to be exported and sent.
The interesting thing is that we’ve seen the quality of the product and the transcription, which allows us to guarantee 96% accuracy, so errors are minimal these days.
Ambient audio capture of clinical encounters is genuinely useful but it's also the highest-stakes category for AI hallucination, a fabricated medication dosage or an incorrectly captured allergy in a clinical note is a patient safety issue, not just a bad output. What's the accuracy validation methodology for ClinicFrame's notes, like how are you measuring and communicating accuracy to clinicians who are relying on these notes for clinical decisions?
ClinicFrame
@ansari_adin
You're separating the right things. A missed nuance in the narrative and a fabricated dosage are not the same class of error, and reporting one accuracy number is how that second one stays hidden.
So we don't track a single score. We look at error rates by content class, and we count omissions and fabrications separately, because they have opposite fixes. For the high-risk classes (medication, dose, allergies, any number) the rule is that nothing enters the note unless it was said in the encounter. The model doesn't complete from prior knowledge, and anything unclear is marked as missing rather than filled in.
The second half of your question matters more than the first. Every line traces back to the audio it came from, so review takes seconds instead of a full re-read, and the note stays a draft until the clinician signs it. Accuracy a clinician can't inspect isn't accuracy, it's a claim.
If you've done validation work in this space, I'd want that conversation.
Not a clinician. I build compliance software, so I'm answering sideways.
The one thing I'd never let a tool do: change anything after a human signed it. Silently, in a background job, with no record of what moved.
Every AI action in my product needs an explicit confirm and an audit log entry. Because "who changed this and when" deserves a better answer than "the model, sometime Tuesday."
A signed clinical note is a legal document. An AI that quietly edits it after the fact hasn't improved it. It's forged it.
You've clearly thought about this already. The part I'd stress-test is post-signature: re-runs, template migrations, retro-enhancement of old notes.
Congrats on the launch.
ClinicFrame
@mlitwiniuk
You're not answering sideways, you're answering the part most people skip.
Acceptance closes the document on our side (the formal signature usually lives in the EHR). After that, nothing is edited in place. A change becomes a new versioned entry with its own author and timestamp, and the original stays exactly as it was accepted. That's the amendment model clinical records have used for decades, and there's no reason for AI to get an exception.
On your three: a better model never re-runs an accepted note, it only touches drafts. A template change applies to future notes and never reformats past ones, so a note keeps the shape it had the day it was accepted. Retro-enhancement is the one we rule out entirely, and it's the hardest of the three, because it never arrives as an attack. It arrives as a well-meaning product suggestion: we could re-run last year's notes with the new model and they'd all be better.
"Forged it" is the right word for that. If you ever want to compare audit trail designs, I'm in.
@mlitwiniuk @clemente_lopez1 the amendment model is the one i'd steal for finance too. a signed clinical note and a posted transaction are basically the same problem, once something's final you don't edit it in place, you append a correction with its own trail. i've seen the opposite happen in financial tools, someone edits a ledger entry after the fact because it's "just a small correction," and then nobody can reconstruct why a balance moved months later.
did you land on append-only from existing clinical documentation standards, or did you arrive at it independently and only later realize it matched how records already worked?
@Granola is known for being sneaky since it doesn't inform participants on the call that it's recording. Does ClinicFrame alert the patients before they are recorded?
ClinicFrame
@himani_sah1
This is a very important question.
By law, the patient must ALWAYS be asked for prior consent before recording the session, and it is the doctor’s responsibility to do so.
In our app, the doctor must also confirm that they have obtained prior consent before starting to record.
Not in healthcare myself, but I have watched this team work and the discipline is the thing. Narrow problem, real users, no attempt to be everything at once. Congrats on shipping. Curious how you think about expanding beyond the note itself once clinicians trust the workflow
ClinicFrame
@artstavenka1
Thanks. The expansion follows the note, it doesn't leave it.
Trust gets earned in order. First the note has to be good enough that you stop editing it. Then the patient context between visits, because the note already holds it and rebuilding that history by hand is the next tax after documentation. Then the revenue side, because the billing code comes out of that same note.
Each step is only allowed by the one before it. We don't get to touch a claim until the note is boring and reliable.