Two-line verdict
Corti is a credible, healthcare-native AI platform that gives developers and health systems speech-to-text, medical coding, ambient documentation and clinical reasoning as composable APIs rather than a single finished app. It is a strong pick for organisations that want to build their own clinical AI on a foundation trained on medical data — and a less natural pick for a small clinic that simply wants an off-the-shelf scribe to switch on tomorrow.
Score breakdown
How Corti scores
Read the scorecard as a build-versus-buy question. Corti scores highly on features and integration because its APIs are specifically trained on clinical language and designed to slot into existing healthcare software, while the ease-of-use score reflects that this is a platform for engineering teams rather than a turnkey app for a front-desk user. These are AI Agent Square editorial scores shown as visible text only. We do not publish an aggregate user rating for Corti because we do not yet hold a verified body of user reviews for it; if you have run Corti in production, you can share your experience through the form on our methodology page and we will fold verified submissions into a future update.
What it is
What is Corti?
Corti is a healthcare-only AI company. Founded in Copenhagen in 2016, it has spent the better part of a decade building speech and language models trained specifically on clinical data, rather than adapting a general-purpose model to medicine after the fact. That single decision — healthcare first, everything else second — is the most important thing to understand about the product. It sits in the healthcare AI agents category, and within it Corti is best understood as the infrastructure layer that other clinical AI products can be built on top of.
In practice Corti is not one tool but a set of composable building blocks delivered as APIs. The headline pieces are clinical speech-to-text that handles medical terminology, accents and the messy reality of a real consultation; medical coding that maps a conversation or note to the correct billing and diagnosis codes; an ambient documentation layer, marketed as Corti Assistant, that listens to a patient encounter and drafts a structured clinical note; and a clinical reasoning capability the company calls FactsR, which surfaces relevant facts and suggestions during an interaction. A developer can use any one of these on its own, or chain several together to create a full ambient-scribe-plus-coding workflow.
Corti has raised around $60 million in venture funding to date, capital that has gone into model training, regulatory and security work, and expansion beyond its original European base. For a buyer the funding signals a vendor with the resources to keep its clinical models current, though as always it is day-to-day accuracy in your own specialty and workflow, not the cap table, that should drive the decision.
The crucial framing is that Corti is an enabling platform, not a finished application you hand to clinicians. If you are a health-tech company, a telehealth provider or a hospital with an engineering team, Corti gives you healthcare-grade primitives so you do not have to train clinical speech and coding models yourself. If you are a small practice that just wants an ambient scribe to turn on, you will more likely consume Corti indirectly — through a product built on it — or choose a more turnkey scribe such as Abridge.
Pricing
Corti pricing in 2026
Corti publishes usage-based pricing, which is unusually transparent for clinical AI. Access is sold as credits consumed across speech and text workflows: audio requests consume credits per minute of audio processed, and text generation consumes credits based on input and output tokens. New developers get $50 in free credits to prototype with, then move to pay-as-you-go, with negotiated enterprise agreements for high volume. In 2025 Corti cut prices, reporting that its clinical reasoning endpoint became 30–40% cheaper per invocation and that base token rates dropped around 20%, so check the live pricing page for current rates.
The honest summary for a buyer is that Corti's model is friendly to teams that want to start small and scale. Because you pay for what you process, a pilot costs little, and costs grow with usage rather than sitting as a large fixed per-seat line item. The flip side is that at high volume a usage model needs forecasting: a busy health system processing thousands of encounters a day should model expected minutes of audio and token throughput against the published rates, and negotiate an enterprise commitment.
| Plan / item | Price | Notes |
|---|---|---|
| Free credits | $50 (one-time) | For prototyping and evaluation |
| Speech-to-text | Credits per minute of audio | Clinical ASR; rate on pricing page |
| Text / reasoning | Credits per input + output token | FactsR reasoning rates cut 30–40% in 2025 |
| Pricing model | Pay-as-you-go credits | No mandatory per-seat licence |
| Enterprise | Custom | Volume commitments, BAA, security review |
Before committing, confirm three things in writing: the current credit rates for the exact endpoints you will use, the terms of a Business Associate Agreement and data-handling guarantees for protected health information, and any volume discounts at your expected scale. For a wider framing of how AI vendors price — per-seat versus usage versus outcome — see our 2026 guide to what AI agents cost.
In depth
Inside the Corti platform: the core capabilities
The reason teams reach for Corti rather than a general AI API is that every layer has been shaped by clinical data and clinical workflow. That matters more than it sounds. A general speech model trained on podcasts and call-centre audio degrades quickly on a cardiology consult full of drug names, dosages and abbreviations; a coding model that has never seen ICD or CPT structure cannot reliably map a note to a billable code. Corti's pitch is that its primitives were built for exactly this.
Clinical speech recognition
The foundation is medical speech-to-text. Corti's automatic speech recognition is tuned for the vocabulary, accents and acoustic conditions of real healthcare settings — multiple speakers, background noise, interruptions, and dense medical terminology. For any product that starts from a spoken encounter, the accuracy of this layer sets the ceiling on everything downstream: a documentation draft or a code suggestion can only be as good as the transcript it is built from. In our framework, healthcare-tuned ASR is the single most important differentiator a clinical AI platform can offer, and it is where Corti's decade of focus is most visible.
Ambient documentation (Corti Assistant)
On top of speech sits ambient documentation. Corti Assistant listens to a patient encounter and drafts a structured clinical note — the kind of ambient-scribe experience that has become the most talked-about use of AI in medicine, because clinician documentation burden is a direct driver of burnout. The promise is that the clinician focuses on the patient while the system captures the encounter and produces a draft note to review and sign, rather than typing during or after the visit. Corti offers this both as a usable assistant and, more importantly for its platform strategy, as capability that other products can embed.
Medical coding
Coding is where Corti's healthcare focus pays off commercially. Translating a clinical encounter into the correct diagnosis and procedure codes is laborious, error-prone and directly tied to revenue and compliance. Corti's coding capability maps documentation to the appropriate codes, with the aim of reducing manual coding effort, under-coding and denied claims. This is closely adjacent to the broader world of revenue-cycle and claims processing automation, and it is a major reason health systems evaluate Corti rather than a generic transcription tool.
Clinical reasoning (FactsR)
The most ambitious layer is reasoning. Corti's FactsR capability is designed to surface relevant clinical facts and suggestions during an interaction — for example prompting for information that has not yet been captured, or highlighting considerations based on what has been said. This moves Corti from passive documentation toward active decision support, which is both its biggest opportunity and the area that demands the most caution: any system that influences clinical decisions sits under heavy scrutiny, and must be validated carefully in your own setting and governed appropriately. Corti's 2025 efficiency gains made this endpoint markedly cheaper to call, which lowers the cost of experimenting with reasoning in a workflow.
Why developers choose a platform over an app
Tied together, these layers explain Corti's market position. A health-tech company building a virtual-care product, a scribe, or a coding assistant faces a brutal build cost if it tries to train clinical speech and language models itself. Corti lets that team consume healthcare-grade AI as APIs and concentrate on its own product and workflow. The trade is ownership of the last mile: Corti gives you excellent primitives, but the user experience, EHR integration, clinician change management and validation are yours to build and own.
Pros & cons
Corti pros and cons
- Healthcare-native models trained on clinical data, not retrofitted
- Composable APIs: use speech, coding, scribe or reasoning independently
- Transparent usage-based pricing with $50 free credits to start
- Strong fit for EHR and telehealth integration via an API-first design
- 2025 price cuts (~20% base, 30–40% on reasoning) improved economics
- Decade of healthcare focus rather than a general model adapted late
- Developer-first: needs an engineering team to build the actual product
- Not a turnkey scribe a small clinic can simply switch on
- Clinical reasoning features require careful validation and governance
- Usage-based costs need forecasting at high encounter volume
- Smaller brand footprint than some US-focused scribe vendors
- Last-mile UX, EHR integration and change management are your responsibility
Integrations
Integrations and ecosystem
Corti is API-first by design, which makes it comparatively straightforward to embed into existing healthcare software, electronic health records and telehealth platforms. Rather than shipping a closed app, it exposes its capabilities as endpoints your systems call, with enterprise agreements covering the security and compliance scaffolding healthcare requires.
Use cases
Where Corti fits best
Fit
Who should use Corti — and who should skip it
Corti is a strong fit if you have engineering resources and want to build clinical AI on a foundation that already understands medicine. Health-tech startups, telehealth providers and health systems with developers get healthcare-grade speech, coding, documentation and reasoning as APIs, plus transparent usage pricing that makes a pilot cheap. If your differentiation is your product and workflow rather than your ability to train clinical models, Corti removes the hardest part of the stack.
You should probably skip Corti if you are a small practice that simply wants an ambient scribe running tomorrow with no development work. In that case a turnkey clinical scribe such as Abridge, or a product built on top of a platform like Corti, will get you to value faster. Equally, if you have no appetite for the validation and governance that clinical reasoning features demand, limit your use to the documentation and coding layers, or wait until you can resource that work properly.
Alternatives
Corti alternatives
Corti competes on two fronts: against turnkey clinical scribes for the documentation use case, and against general AI platforms for the build-your-own use case. These are the alternatives we would weigh against it.
Verdict