Corti

Corti is a healthcare-focused artificial intelligence company that develops clinical AI models, APIs, and infrastructure for organizations building healthcare applications. Founded in 2016, the company is headquartered in Copenhagen, Denmark, and focuses on helping healthcare providers and technology developers automate complex clinical and administrative processes. Its platform provides tools for medical speech recognition, clinical documentation, medical coding, text generation, and AI-powered workflow orchestration. Rather than functioning only as a standalone medical assistant, Corti provides technology that developers can integrate into electronic health records, virtual care platforms, practice management systems, ambient scribing applications, and other healthcare software.

The platform is designed for environments where accuracy, privacy, traceability, and dependable performance are important. Healthcare applications handle sensitive patient information and often need to produce structured outputs that can be reviewed, stored, or used in clinical workflows. Corti develops specialized models for these requirements, allowing software companies and healthcare organizations to incorporate medical AI capabilities without having to build their own speech recognition systems, language models, or agent infrastructure from the beginning.

One of Corti’s main offerings is its Symphony family of healthcare AI models. Symphony supports several related functions, including speech-to-text, clinical text generation, medical coding, and clinical reasoning. These capabilities can be used independently through APIs or combined to create more advanced healthcare applications. For example, a developer can use speech recognition to capture a consultation, generate a structured clinical note from the resulting transcript, and then use medical coding capabilities to identify relevant diagnosis and procedure codes. The different components can work together within one application and share a common development environment.

Corti’s Symphony for Speech-to-Text is designed specifically for medical conversations and clinical dictation. General-purpose speech recognition systems can struggle with medical terminology, medication names, abbreviations, measurements, and specialist vocabulary. Corti addresses these challenges through speech recognition models trained and evaluated for clinical language. The technology can process live conversations, spoken dictation, and previously recorded audio, making it useful for ambient documentation systems, virtual consultations, voice-controlled applications, and medical transcription software.

The speech-to-text API provides different processing methods for different development requirements. Its real-time dictation endpoint allows applications to receive transcripts while a clinician is speaking. The conversational streaming endpoint supports ongoing interactions and can distinguish between speakers, which is useful when a consultation involves a clinician and a patient. A separate batch-processing endpoint allows developers to submit recorded audio and retrieve transcripts asynchronously. These options make it possible to build applications for both live and pre-recorded clinical audio without maintaining separate speech recognition providers.

Symphony also includes contextual recognition features. Developers can supply terminology lists containing medication names, facility names, clinician identifiers, abbreviations, and other specialist vocabulary. The model can use this context to improve recognition of terms that might otherwise be misinterpreted. This is particularly valuable in healthcare environments where a small transcription error can change the meaning of a clinical statement. Corti also supports spoken punctuation, automatic formatting, replacement rules, custom dictionaries, and structured recognition outputs.

Speaker diarization is another important speech recognition capability. It identifies and separates different speakers in a conversation, helping applications distinguish between statements made by a patient and those made by a healthcare professional. This can make generated consultation notes easier to organize and review. Corti also provides interim transcription results, allowing applications to display recognized words while an interaction is still taking place rather than waiting until the entire recording has finished.

The platform includes audio quality monitoring through Audio Health Events. These signals help applications identify potential problems with incoming audio before an unreliable transcript is passed to another part of the workflow. This can be useful when a microphone is unclear, background noise affects a recording, or an interaction contains other audio problems. Developers can use these signals to prompt a user to correct the input or handle the recording appropriately.

Another major component is Corti’s Text Generation API. It transforms transcripts, patient information, and structured data into clinical documents and other healthcare-related text. Developers can use it to generate consultation notes, patient summaries, referral letters, discharge information, and other documents according to the requirements of their applications. The API is designed to produce structured outputs that can follow specific templates, helping software teams build documentation features that fit into existing clinical workflows.

Corti’s text generation capabilities also support clinical information extraction and summarization. Applications can use these functions to organize information from patient records, identify relevant facts, and prepare concise summaries for professional review. For example, a pre-charting application can summarize information from previous encounters before a patient appointment. A clinical documentation improvement application can review notes for missing or incomplete information. A patient communication system can generate letters and explanations based on information supplied by the healthcare organization.

The Corti platform also provides medical coding capabilities. Medical coding involves translating clinical information into standardized codes used for documentation, billing, reporting, and administrative processes. Corti’s Medical Coding API can return structured predictions for code sets such as ICD-10, CPT, and SNOMED CT, depending on the supported workflow. It also provides supporting evidence and an audit trail to help users understand why a particular code was suggested. This is important because coding decisions need to be traceable and suitable for review rather than appearing as unexplained automated outputs.

The coding technology can support several activities, including diagnosis identification, procedure extraction, code validation, and review of documentation against coding requirements. Healthcare software developers can use these capabilities to build coding assistants, revenue cycle applications, and documentation review systems. The platform can also help identify information that may be relevant to a coding decision, allowing qualified professionals to examine the supporting evidence before accepting the result.

Corti’s Agentic Framework provides tools for building AI agents that can reason through tasks, use connected systems, and coordinate multi-step workflows. Instead of generating a single response, an agent can receive a task, gather relevant information, perform supported actions, and return a result. This capability can be applied to administrative and clinical processes such as referral coordination, prior authorization preparation, denial appeals, patient follow-ups, and clinical documentation review.

The platform includes a library of pre-built agent blueprints that developers can adapt to their own applications. These include medical coding agents, clinical guideline checkers, triage and initial assessment agents, referral generators, denial appeals agents, medication reconciliation assistants, and prior authorization assistants. Other examples include nursing shift handoff summaries, ICU admission summaries, note completeness checks, and clinical documentation improvement workflows. These starting points can help development teams build specialized applications without designing every workflow from the beginning.

Corti’s agent infrastructure is intended to provide governance and control over automated actions. Developers can connect agents to external systems through supported connectors and integration methods. The framework supports tools such as MCP and A2A compatibility, allowing applications to communicate with other services and coordinate tasks. It also provides guardrails, execution controls, and audit trails so organizations can monitor how an agent performs a workflow. These features are especially important in healthcare, where automated actions may affect patient records, billing processes, or administrative decisions.

The platform can support a wide range of healthcare applications. Electronic health record vendors can integrate speech recognition, clinical documentation, and coding into their products. Virtual care platforms can use the technology to transcribe remote consultations and prepare structured notes. Practice management software providers can incorporate documentation, billing, and administrative automation. Developers of ambient scribing applications can use Corti’s clinical speech recognition as the foundation of their products instead of maintaining their own speech processing infrastructure.

Revenue cycle management is another area where Corti can be useful. Healthcare organizations need to manage coding, claims, prior authorizations, and insurance-related documentation. Corti’s models and agent blueprints can support coding workflows, prepare denial appeal documents, and help validate information before claims are submitted. The technology can also support eligibility and claims validation applications, depending on how developers configure their systems and connect the necessary data sources.

Corti also provides capabilities for patient engagement. Developers can use its text generation and agent infrastructure to build conversational patient assistants, reminders, follow-up systems, adherence support tools, and patient education applications. These applications can help healthcare organizations maintain communication with patients beyond the consultation itself. Patient-facing outputs should still be configured with appropriate clinical safeguards and reviewed according to the intended use of the application.

Clinical decision support is another potential application. Corti’s agent blueprints include tools for checking clinical guidelines, summarizing results, identifying trends, and supporting treatment planning. These functions can help organize information for healthcare professionals, but they should not be treated as independent replacements for clinical judgment. Medical professionals remain responsible for reviewing relevant information and making decisions about patient care.

The platform also offers Corti Models, a collection of AI models hosted on European infrastructure. These models are designed for developers who want access to AI inference while maintaining greater control over where their data is processed. Corti provides an OpenAI-compatible API, allowing supported applications and development tools to connect by changing their endpoint and authentication configuration. This can make it easier for teams to use Corti’s models within existing AI development environments.

Corti provides developer resources to make integration more straightforward. Its official documentation explains how to create an account, configure API credentials, authenticate requests, and use the available endpoints. The company also offers official JavaScript and C# .NET SDKs. These tools support development across different application environments and help manage authentication, WebSocket connections, requests, and other implementation details. Developers can also use its documentation, API references, quickstarts, and Postman resources when building and testing integrations.

Security and data governance are important parts of Corti’s platform. Healthcare applications may process sensitive patient information, so organizations need to consider how data is stored, transmitted, and accessed. Corti describes security measures such as encryption, customer-specific keys, role-based access, tenant isolation, and audit logging. It also offers deployment options involving European or United States hosting, customer-selected cloud infrastructure, and on-premises environments, depending on the product and agreement. Its compliance materials identify standards and regulatory frameworks relevant to healthcare and other regulated industries, including HIPAA, GDPR, and SOC 2. Organizations should review the applicable certifications, contractual terms, and deployment arrangements before using the platform with sensitive information.

Corti was founded in 2016 by Lars Maaløe and Andreas Cleve. The company developed its technology through research into clinical speech recognition, machine learning, and decision support. Over time, its work expanded from voice-based assistance for healthcare professionals into a broader AI infrastructure platform for clinical software developers and enterprises. Its current offerings reflect that expansion, bringing together speech processing, documentation, coding, AI agents, and model infrastructure.

The platform’s main advantage is that it provides specialized healthcare AI components through a developer-oriented environment. Instead of relying on one general-purpose model for every task, organizations can use different capabilities for transcription, document generation, coding, and workflow automation. This modular approach can help developers create products that address specific operational needs while retaining control over their own user interfaces and application logic.

Corti is not a complete electronic health record system or a substitute for a hospital’s entire technology environment. It provides models, APIs, and development infrastructure that other applications can use. Organizations generally need developers or technical partners to connect the services to their software, configure workflows, and establish appropriate review procedures. The quality of the final application will depend on its integration, the information supplied to the models, and the safeguards implemented by the development team.

Overall, Corti is a specialized healthcare AI platform for developers, software companies, and healthcare organizations that want to build clinical and administrative applications. Its combination of medical speech recognition, structured documentation, medical coding, agentic workflow automation, and deployment controls makes it relevant to EHR vendors, virtual care providers, ambient scribe developers, practice management platforms, and revenue cycle businesses. It is particularly useful for teams that need healthcare-specific AI capabilities through APIs rather than a standalone general-purpose chatbot.

Pricing

Corti uses a usage-based credit system across its APIs. New users receive $50 in free credits without needing to provide a payment card. Once the credits are used, API usage can continue through pay-as-you-go billing.

Published API rates include:

• Speech to Text: $0.0065 per audio minute.
• Medical Coding: $4 per million input tokens and $16 per million output tokens.
• Agents: $4 per million input tokens and $16 per million output tokens.
• Text Generation: $4 per million input tokens and $16 per million output tokens.

Corti also offers an Acceleration Pack for $1,000 per month, which includes 1,000 credits, five hours of developer support per month, and an architecture review. Corti Advance provides embedded engineering support under a monthly commitment, while Enterprise offers customized credit bundles, higher concurrency, custom-trained models, priority throughput, and sovereign cloud deployment options.

Credits are shared across supported APIs and are valid for one year. Actual costs depend on the endpoints used, audio duration, token consumption, and application workload.

Review

Corti is a strong healthcare AI infrastructure platform for organizations that want to build clinical software with specialized AI capabilities. Its main advantage is the combination of medical speech recognition, text generation, coding, and agentic workflow automation within one developer-oriented platform. This gives software teams access to several related capabilities without having to maintain separate AI providers for every part of their applications.

Its Symphony speech-to-text technology is particularly relevant to developers building medical dictation and ambient documentation products. The support for real-time streaming, batch processing, speaker diarization, and clinical terminology makes it suitable for different types of healthcare audio workflows. Contextual vocabulary and structured formatting are useful additions because medical applications need more than a basic word-for-word transcript.

The Text Generation API and Medical Coding API also add practical value. Developers can use them to generate clinical documents, summarize patient information, extract relevant facts, and suggest standardized medical codes with supporting evidence. The availability of structured outputs and audit trails is beneficial for applications where users need to review how information was generated.

Another strength is the Agentic Framework. Its pre-built blueprints provide starting points for common healthcare tasks, including referral coordination, prior authorization, coding, documentation improvement, and denial appeals. The ability to connect agents to external systems through connectors and supported protocols gives development teams flexibility when creating more advanced workflows.

Corti’s pricing is relatively accessible for initial development because new users receive $50 in free credits. The pay-as-you-go structure also allows teams to test different APIs without immediately committing to a large subscription. However, costs can increase as audio volume, token usage, and production workloads grow. Organizations that need additional engineering support or enterprise deployment features may also need to consider the higher-cost packages.

The documentation and official SDKs are further advantages. JavaScript and C# .NET support gives developers practical options for integrating the platform into existing applications. API references, quickstarts, and implementation guides can reduce the effort involved in configuring and testing the services.

There are also limitations to consider. Corti is primarily a developer platform, so organizations without technical resources may find it more difficult to use than a ready-made clinical application. Building a complete solution requires integration work, application design, testing, and ongoing maintenance. Healthcare organizations also need to configure suitable safeguards and review processes before using AI-generated content in clinical or administrative workflows.

Language coverage is another consideration. Corti’s Symphony speech-to-text service supports more than 14 languages, but its public product information does not provide a complete, easily verifiable list of every supported language. The strongest published speech-recognition benchmark examples include English, French, and German. Organizations that require other languages should confirm availability and performance for their intended clinical use.

Finally, AI-generated transcripts, summaries, and coding suggestions should not automatically be treated as final professional decisions. Even when a model provides supporting evidence, healthcare teams need to review outputs according to their clinical, legal, and operational requirements. The platform is best understood as infrastructure that supports healthcare professionals and software developers rather than a replacement for qualified medical judgment.

Corti is a good option for healthcare technology companies and development teams that need specialized clinical AI APIs. Its broad functionality, usage-based pricing, SDKs, and agent framework make it suitable for building products that combine speech recognition, documentation, coding, and workflow automation. Teams should evaluate integration requirements, language coverage, data governance, and expected usage costs before choosing it for production.