Corti supplies healthcare AI APIs for speech, documents and agent workflows

Corti provides infrastructure for developers building healthcare and life-science applications: speech recognition, structured document generation, medical coding, agent workflows and model access. The company’s product is the API platform and supporting tools around these capabilities. A team using it still owns the application, its user experience and the evidence required for its intended workflow.

01 / ProductThe platform separates capture, structure and action

The API introduction describes a single platform spanning speech, text generation, medical coding, agents and an embeddable assistant. Corti also offers model access through an OpenAI-compatible interface. These capabilities belong to one company identity, but they solve different layers of a product. Transcribing audio, composing a document and acting on that document should remain distinct stages in an application’s design.

Speech to Text has separate interfaces for stateless live dictation, stateful conversational streaming and asynchronous recorded audio. The product page describes REST and WebSocket access, speaker diarization in the conversational path and audio-quality events. A developer should choose the interface according to session behavior: a voice command is different from a long encounter whose context and speakers must remain associated.

Text Generation accepts transcripts, facts or structured context and produces documents using standard or custom templates. It can create more than one output from an interaction, such as a professional note and a patient-oriented summary. The product distinction is controllable structure around the generated text. The application must still make the outputs reviewable and prevent one unapproved document from becoming another workflow’s authoritative input.

02 / AudienceA developer platform fits teams that want to own the application

Corti is relevant when an organization has engineering capacity and wants to integrate language capabilities into an existing product. A virtual-care vendor might already manage encounter identity, appointments and clinical review, but need speech and document infrastructure. An EHR vendor may want voice input inside its own interface rather than send users to a separate application. These are different integration projects even when they call the same APIs.

The platform is less direct for a solo clinician whose immediate requirement is a ready-to-use scribe subscription. The Abridge blueprint illustrates a packaged health-system workflow, while Deepgram provides a comparison for speech infrastructure. The useful comparison is what the buyer must build around the model: identity, interface, review, data handling and operational support.

Owning the application gives a team control over those decisions, but it also creates ongoing work. A model response must be associated with the correct session, displayed in a way the intended reviewer understands and handled appropriately when a request fails. The API’s healthcare positioning does not transfer all responsibility for the finished product to Corti.

03 / WorkflowA proposed build keeps the encounter and its outputs traceable

Imagine a proposed ambient-documentation prototype using synthetic encounter material. Start by creating a clear application-level interaction record and selecting a region. Keep that identifier with the audio stream, transcript and generated documents. The purpose is not to reproduce Corti’s internal architecture; it is to ensure that the application can explain which input produced each output and which version a reviewer approved.

Use the appropriate speech path. A continuous conversational session needs different handling from an uploaded recording or a brief dictation command. Surface audio-health information in a useful way rather than silently continuing when input quality deteriorates. In the prototype, interrupt a stream and resume it deliberately so the team can observe whether partial results, retries or duplicated text are exposed to the user.

Generate one document with a defined template, then inspect it against the source. Add a second output only after the first is reviewable. The document-generation description supports multiple outputs from the same encounter, but their audiences differ. A patient summary may need simpler language and a different selection of information than a professional record. That difference should be explicit in the template and review process.

If coding is included, treat the returned code and its evidence as another reviewable artifact. Corti’s coding API describes structured predictions with supporting material and justification. Do not automatically convert a suggested code into a submitted claim merely because the response is well formed. The application needs an explicit destination, reviewer and acceptance rule for this output.

Record input duration, token consumption, retries and review effort across representative cases. A short clean recording can understate both cost and implementation difficulty. Include longer encounters and revisions so the team understands how context length and multiple document requests affect the metered bill. This is proposed engineering work, not a claim that Sequenced has run a Corti implementation.

04 / PricingMinutes and tokens should be budgeted separately

The current tariff lists speech at US$0.0065 per minute. Medical coding, agent and text-generation API rates are shown at US$4 per million input tokens and US$16 per million output tokens. These units should not be collapsed into a universal per-encounter price: an application can make several requests and produce more than one document from the same audio.

For illustration, a 20-minute transcription at the published speech rate costs US$0.13 before any text-generation or other usage. This arithmetic is an example, not a vendor encounter bundle. The text bill then depends on the input and output tokens actually processed, including the context the application supplies. Repeated generation and long historical records can change that part of the cost substantially.

Corti describes a shared balance, US$50 of trial credits and purchased credits valid for one year. It also lists an Acceleration Pack at US$1,000 per month with 1,000 credits and five development-support hours. Enterprise commitments and Corti Advance engineering support have separate scoping routes. Model-hosting rates differ from the healthcare endpoint tariff, so price the selected endpoint rather than using one rate for the entire platform.

05 / DistinctionsHealthcare-specific interfaces are more useful than a broad accuracy slogan

The speech product’s division into dictation, conversational streaming and batch processing makes implementation choices explicit. Audio-health events and context-sensitive terminology are meaningful features to inspect because errors can originate before document generation begins. A system that notices poor capture gives the application an opportunity to respond; a polished note generated from incomplete input may hide the problem.

The official SDK overview lists JavaScript and .NET SDKs that handle common connection and authentication work. That reduces some plumbing, but the page labels the Agent SDK as private preview. An engineering plan should distinguish generally available APIs and SDKs from preview access. The presence of agent examples does not mean every development tool is available without a conversation with Corti.

The infrastructure page describes regional hosting options, access controls and audit logging. These are vendor statements about the platform. The application’s own logs, storage and external services remain part of the actual data path. A regional API choice is useful only if the rest of the configured workflow preserves the intended boundary.

06 / QuestionsModel regions and preview tooling need explicit verification

The current documentation says Corti Models behaves differently by region: EU projects can use sovereign EU infrastructure, while US projects reach Azure-hosted OpenAI models under the same model identifiers. This is a consequential availability distinction. A compatible request format or shared model name should not be interpreted as proof of identical underlying infrastructure or model behavior across regions.

Corti publishes comparative speech and coding performance claims. They are not reproduced here as independent rankings. A developer should review the task, dataset, language, configuration and model version behind any benchmark being used to justify a product decision. Medical terminology recognition and the quality of a complete generated document are different evaluations.

The final open question is operational fit. Establish throughput limits, retention settings, version-change behavior and the agreement required for the intended data before production use. These are especially relevant when the application combines speech, documents and agents: a successful transcription does not prove that a downstream action completed, and a retry should not create a second irreversible action.

07 / DecisionChoose Corti when control over the product is part of the requirement

Corti is a strong option to investigate for a team building healthcare software that wants managed language infrastructure while retaining its own application. Start with one complete, reviewable interaction path and measure its actual API consumption. Add more outputs and automation only when the source, review state and failure behavior remain understandable.