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Today, Copenhagen-based healthcare AI Corti is launching Symphony for Speech-to-Text, a new generation of clinical-grade speech recognition models engineered specifically for real-time dictation, conversational transcription, and batch audio processing — and their accuracy rate is the highest for this specific use case yet recorded.
“We are focused on ensuring our AI scribes can be trusted by physicians, medical practitioners and patients…the entire healthcare system,” said Andreas Cleve, co-founder and CEO of Corti, in an exclusive video call interview with VentureBeat.
The performance data the company is bringing to the table paints a stark picture of the current state of enterprise AI: when it comes to highly regulated, specialized industries, domain-specific models can beat out the foundation model providers.
In a newly published research paper, Corti revealed that its new clinical-grade speech models reduced word error rates (WER) by up to 93% when compared against leading generalist speech models and APIs on medical terminology.
On English medical terminology, its Symphony for Speech-to-Text achieved a remarkably low 1.4% WER. By comparison, OpenAI’s speech model registered a 17.7% WER, ElevenLabs hit 18.1%, Whisper recorded 17.4%, and Parakeet scored 18.9%.
Corti’s announcement serves as a critical inflection point for healthcare builders. While general-purpose APIs like OpenAI’s whisper are sufficient for broad-domain transcription, they frequently stumble over medical acronyms, complex medication dosages, shorthand, and noisy emergency room environments. Symphony for Speech-to-Text aims to solve this by providing developers with a highly specialized, production-grade API designed from the ground up for clinical workflows.
The launch of Symphony for Speech-to-Text highlights a fundamental shift in how healthcare uses voice technology. For decades, medical speech recognition was primarily about generating a static text document for human doctors to review—a digital replacement for a notepad.
But as the healthcare industry hurtles into what technologists call the “agentic era,” where autonomous AI agents actively assist in clinical decision-making, EHR navigation, and real-time support, the transcript is no longer the final product. It is the foundational data layer.
“Speech has always been one of healthcare’s most important inputs,” Cleve said in a statement provided to VentureBeat. “What is changing is what happens after the words are captured. In the agentic era, speech recognition requires more than simply producing a transcript – we need to give AI systems accurate clinical facts to reason from. If a model mishears a medication, dosage, or symptom, every downstream step becomes less reliable. Symphony for Speech-to-Text gives healthcare builders a speech layer accurate enough to thrive in clinical reality.”
This is where the compounding danger of high word error rates comes into play. If a general-purpose AI model hallucinates a transcription—turning “hyperthyroidism” into “hypothyroidism,” or misinterpreting a critical medication dosage—every subsequent AI agent relying on that transcript will operate on corrupted data. Corti’s architecture mitigates this risk by producing structured, clinically usable output directly from the API, helping downstream AI applications reason over clean facts rather than messy, unformatted text.
Nowhere is this more evident than in Corti’s entity recall benchmarks. Symphony for Speech-to-Text reached an astonishing 98.3% recall rate on formatted clinical entities—such as dosages, measurements, and dates. In contrast, Corti reported that the strongest general-purpose baseline model maxed out at just 44.3% recall for the same entities.
For developers building ambient AI documentation tools, that 54% gap is the difference between a tool that saves a physician time and a tool that constitutes a medical liability.
While Corti’s benchmarks against modern LLM builders like OpenAI and ElevenLabs are striking, the company is also taking aim at legacy medical transcription giants.
For years, the gold standard for dedicated clinician dictation has been Dragon Medical One. However, these legacy systems were historically optimized strictly for intentional clinician dictation, not as underlying infrastructure for ambient AI, complex multi-party conversations, or real-time clinical support tools.
In evaluations of real-world English medical dictation, Corti achieved a 4.6% WER, outperforming Dragon’s 5.7% (a 19% relative improvement).
Furthermore, Corti demonstrated a higher medical term recall than Dragon (93.5% versus 92.9%).
By providing this level of accuracy via an API endpoint, Corti is enabling third-party developers, EHR vendors, and virtual care platforms to build their own custom dictation and ambient listening tools that outperform the industry’s legacy incumbent.
“We want people to build apps atop our models,” Cleve said. “The goal is to diffuse the technology as widely as it is needed so it can be as helpful as possible to patients and their doctors and professionals.”
For Cleve and his co-founders, the mission is a personal one: Cleve’s own mother was a healthcare professional attacked by a patient and spent years struggling to recover. He sought to improve healthcare processes as a way of honoring her sacrifice.
The demands of healthcare extend far beyond English-speaking hospitals, and global health systems have historically been underserved by clinical NLP models. Early adopters are already leveraging Corti’s new models in linguistically demanding environments, proving the technology’s viability in complex international markets.
Switzerland, for instance, requires care delivery across multiple languages—often simultaneously within a single medical institution. It serves as one of the most stringent proving grounds for multilingual medical speech models in the world. Corti’s Symphony models demonstrated massive performance gains in these non-English tests, achieving a 2.4% WER in German (compared to 13.0% for the next-best system) and a 3.9% WER in French (versus 10.6%).
“In a clinical conversation, every word matters – a missed medication name, a misheard dosage, or a mistranscribed symptom can change the meaning of an encounter,” said Pierre Corboz, Head of Solutions & Business Development at Voicepoint, a Swiss healthcare technology provider, in a statement provided to VentureBeat. “Symphony’s accuracy on clinical terminology gives us the foundation to bring more trusted AI capabilities into clinical workflows with our Voicepoint Xenon platform. When Corti improves the speech layer, the workflows we build together become sharper, safer, and more useful for clinicians in Switzerland.”
Today’s announcement of Symphony for Speech-to-Text is not an isolated event; it is the culmination of a strategic narrative Corti has been aggressively pushing over the last several weeks.
The broader Symphony platform—which powers clinical and administrative applications for a global network of EHR vendors and life sciences organizations—has been systematically proving the defensibility of vertical AI labs against horizontal tech giants.
This marks the third major benchmark Corti has released in just six weeks, touching different layers of healthcare AI performance.
In April, the company revealed that its Symphony for Medical Coding system outperformed general-purpose models by more than 25% in clinical accuracy benchmarks, tackling one of healthcare’s most notoriously complex workflows.
And just last week, Corti announced that its flagship clinical-grade model outscored OpenAI on HealthBench Professional, OpenAI’s own healthcare benchmark.
Taken together, these three data points—medical coding, clinical reasoning, and speech-to-text accuracy—illustrate a growing consensus in the enterprise technology sector: generalized models are hitting a ceiling in regulated industries.
Models deployed in hospitals must inherently understand complex acronyms, sudden interruptions, medical shorthand, specialty-specific language, and strict compliance constraints. By training specifically on these unique edge cases, vertical AI labs like Corti are building a formidable moat that companies relying solely on API calls to generalized large language models cannot easily cross.
Developers are clearly taking notice of the performance gap. According to momentum data provided to VentureBeat, Corti is seeing a 30% growth in new sign-ups for its platform in quarter-to-date comparisons, signaling that developers and healthcare builders are actively gravitating toward vertical, clinical-grade models over generalist APIs.
Corti, which already serves over 100 million patients annually across major health systems including the UK’s National Health Service (NHS), is positioning Symphony for Speech-to-Text as the default engine for the next generation of healthcare software.
It is important to note that Corti is not launching the overarching Symphony platform itself today; rather, Symphony for Speech-to-Text operates as a new, distinct capability within that broader ecosystem, accessible via its own API endpoints.
Symphony for Speech-to-Text is generally available starting today. Developers and enterprise architects can access the models via the Corti API console, with full technical documentation available to help integrate the clinical-grade speech layer into their existing applications.
In a move toward research transparency, Corti has also published its full research paper detailing its methodology, along with a separate comparison tool designed to support transparent evaluation of medical speech recognition systems across the industry.
As the healthcare industry continues its rapid embrace of AI-driven automation, the foundational data layer has never been more critical. Corti’s latest launch is a stark reminder that in the medical field, generic AI simply isn’t good enough. The future belongs to the specialists.
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Google on Tuesday unveiled Gemini Spark, a personal AI agent designed to work around the clock — drafting emails, assembling documents, monitoring inboxes, and eventually making purchases — even when a user’s laptop is closed and their phone is locked.
The announcement, made at Google I/O 2026, is the company’s most ambitious attempt yet to transform its AI assistant from a tool that answers questions into one that autonomously completes tasks. It also arrives at a moment of extraordinary competition, as Microsoft, OpenAI, Anthropic, and Apple all race to build AI systems that don’t merely converse but act — completing multi-step workflows with decreasing human supervision.
“We are in that part of the cycle where people want to see real value in the products they use on a day-to-day basis,” Sundar Pichai, CEO of Google and Alphabet, said during a press briefing ahead of the keynote address. With Spark, he argued, that value comes from an agent that never stops working. It operates around the clock in Google’s cloud, he said, so “you don’t need to keep your laptop open to make sure it’s running.”
The product arrives at an inflection point for the technology industry, as Google, Microsoft, OpenAI, Anthropic, and Apple all race to build AI systems that don’t merely converse but do — completing multi-step workflows with decreasing human supervision. It also raises urgent questions about trust, spending guardrails, and what happens when an artificial intelligence agent misinterprets a user’s intent.
Spark will begin rolling out this week to a small group of trusted testers, with a beta planned for Google AI Ultra subscribers in the United States next week.
Unlike conventional AI assistants that activate only when prompted, Gemini Spark is architecturally different. It runs persistently on Google Cloud infrastructure, powered by the company’s new Gemini 3.5 Flash model and what Google calls the Antigravity agent harness — the same underlying system that powers the company’s internal developer tools.
In practical terms, this means Spark can accept a complex instruction — “email my boss a status update pulling the latest figures from our shared spreadsheet and the project timeline in our Slides deck” — and then execute it across multiple Google applications without further input. The agent can pull context from emails, documents, and calendar entries, synthesize the information, and produce a finished output.
Josh Woodward, VP of Google Labs, Gemini App, and AI Studio, described the experience in visceral terms during the briefing: “When you use it, it almost feels like you’re tossing things over your shoulder — Spark’s catching them and gets the job done.”
The cloud-based architecture is a deliberate design choice. Because Spark operates on remote servers rather than on a user’s device, it can continue working through tasks after a user walks away. A student could ask Spark to build a study guide that updates itself as new assignments arrive from a professor. A small business owner could instruct it to monitor their inbox and flag potential customer inquiries. A parent could delegate the logistics of a neighborhood block party — tracking RSVPs, coordinating contributions, scouting venues. These are not hypothetical scenarios. Woodward said they reflect how early testers have actually been using the product.
Over the coming months, Google plans to expand Spark’s capabilities significantly. The company will roll out MCP (Model Context Protocol) connections to more than 30 third-party partners, including Canva, OpenTable, and Instacart. Users will also be able to text and email Spark directly, create custom sub-agents for specialized tasks, and connect Spark to Chrome for web-based actions. Later this year, a new Android interface called Android Halo will provide live, at-a-glance visibility into what Spark is working on, displayed at the top of a user’s phone screen.
For all its ambition, Spark confronts a fundamental challenge that has bedeviled every AI agent to date: How do you trust an autonomous system to act on your behalf — particularly when money is involved?
Google is acutely aware of the concern. When asked during the press briefing how Spark would avoid making unauthorized purchases, Woodward reached for an analogy that was striking in its candor. “On the team, we think a lot of it is like if you’re giving a teenager their first debit card — there’s sort of limits and sort of constraints around it, and that’s how we’ll be designing Spark as we go through the year,” he said.
At launch, Spark will not autonomously make purchases. Users will be given explicit opportunities to review and approve any transaction before it goes through. But Google has built the infrastructure for a more autonomous future. Vidhya Srinivasan, who leads Google’s ads and commerce teams, introduced the Agent Payments Protocol, or AP2 — a system designed to let AI agents make secure purchases within user-defined boundaries.
The concept works like this: a user tells their agent the specific brands, products, and spending limits they’re comfortable with. If the criteria are met, the agent can automatically complete a purchase. AP2 creates what Google describes as a transparent, verifiable link between the user, the merchant, and payment processors, using privacy-preserving technology and tamper-proof digital mandates to ensure the agent is acting within its authorization. AP2 also generates a permanent digital paper trail, so that if a return is needed, the user and the merchant are looking at the same record. Google plans to bring AP2 to its products in the coming months, starting with Gemini Spark.
The system is underpinned by the Universal Commerce Protocol (UCP), an open-source standard Google announced earlier this year that gives agents and commerce systems a common language across the entire shopping journey. The UCP Tech Council now includes Amazon, Meta, Microsoft, Salesforce, and Stripe — a remarkable coalition that underscores how seriously the industry takes the prospect of agent-driven commerce.
Google also announced the Universal Cart, an intelligent shopping cart that works across merchants and Google services. Users can add items while browsing Search, chatting with Gemini, watching YouTube, or reading Gmail. The cart then works in the background — tracking price drops, surfacing deals based on payment card perks, and even flagging product incompatibilities. The shopping infrastructure is rolling out in the U.S. this summer across Search and the Gemini app, with YouTube and Gmail to follow.
The announcement lands in the middle of the most intense competitive period in AI history. Google, Microsoft, OpenAI, Anthropic, and Apple are all racing to ship autonomous agents that can do real work — and each is placing a fundamentally different architectural bet on how to get there.
OpenAI recently unified its Operator and deep research capabilities into ChatGPT agent — a system that brings together website interaction, information synthesis, and conversational intelligence. It carries out tasks using its own virtual computer, shifting between reasoning and action to handle complex workflows. The company emphasizes that users remain in control, with ChatGPT requesting permission before taking consequential actions. But the product has faced scrutiny over reliability. OpenAI’s Computer-Using Agent scores 38.1% on OSWorld, the industry benchmark for computer use tasks, while humans score over 72%.
Anthropic launched its Claude Computer Use Agent in research preview in March, giving Claude the ability to see, navigate, and control a user’s desktop — clicking buttons, opening applications, filling spreadsheets, and completing multi-step workflows. Claude Cowork handles tasks autonomously — users give it a goal and Claude works on their computer, local files, and applications to return a finished deliverable. Anthropic has iterated aggressively, recently shipping ten pre-built financial agents and pursuing deep Microsoft 365 integration.
Microsoft introduced Copilot Cowork to move beyond chat and into execution — helping users delegate real tasks and have them completed. Cowork runs in the cloud, meaning users don’t have to worry about closing their laptop. The system is grounded in Work IQ, Microsoft’s intelligence layer that understands organizational data, tools, and structure. The shift moves Copilot from a sidebar helper to an orchestrator of autonomous agents.
Apple is also preparing a revamped Siri for WWDC 2026 that will act as an “always-on agent” capable of handling tasks across apps using personal data. Google’s Gemini models will help power the upgraded Siri through a multi-year deal reportedly costing Apple around $1 billion per year.
The convergence is unmistakable: every major platform is moving from assistants that talk to agents that act. But each is approaching the problem differently. OpenAI’s agent operates primarily through a browser. Anthropic’s works directly on a user’s desktop. Microsoft’s is tightly bound to the Office 365 ecosystem. Apple’s emphasizes on-device processing and privacy. Google’s approach with Spark is distinctive in its bet on cloud persistence and deep integration with its own services.
Rather than controlling a user’s screen pixel by pixel, Spark works through structured integrations — Google’s own Workspace APIs, and increasingly, third-party connections through MCP. The advantage is reliability and speed: structured tool use is far more predictable than screen-reading. The disadvantage is that Spark, at least initially, can only act within the systems it’s been connected to.
Spark’s capabilities are inseparable from the model that drives it. Gemini 3.5 Flash, also announced Monday, is Google’s new workhorse AI model — designed specifically for the demands of agentic workflows.
The performance claims are important. Google says 3.5 Flash outperforms its previous frontier model, Gemini 3.1 Pro, across nearly all benchmarks, while running four times faster than comparable frontier models in terms of output tokens per second. An even more optimized version, available within Google’s Antigravity development platform, runs twelve times faster.
Pichai framed the economics bluntly. Companies processing roughly one trillion tokens per day on Google Cloud — a figure he said top enterprise customers are hitting — could save over $1 billion annually by shifting 80% of their workloads to a mix of Flash and frontier models like 3.5 Pro. In a market where, as Pichai noted, CIOs are already “blowing through their annual token budgets and it’s only May,” the cost argument may matter as much as the capability argument.
Internally, Google’s own developers have been consuming Gemini 3.5 Flash at a staggering and rapidly accelerating pace. In March, Google was processing about half a trillion tokens per day internally. That figure has since grown to more than three trillion — doubling roughly every few weeks. Pichai described this as a “powerful feedback loop” that continually improves the model.
Koray Kavukcuoglu, CTO of Google DeepMind and Chief AI Architect for Google, said the model’s speed is what makes agentic use cases practical. “3.5 Flash is especially good when deploying multiple agents simultaneously and completing long-running tasks,” he said during the briefing, adding that Google had successfully tested agents building “a working operating system entirely from scratch.”
The 3.5 Pro model, the more powerful sibling, is currently being tested internally and will roll out next month.
Gemini Spark will be available to Google AI Ultra subscribers. The company is simultaneously restructuring its subscription tiers to make the technology more accessible. A new Ultra plan at $100 per month provides a 5x higher usage limit than the Pro plan, along with priority access to Antigravity and 20TB of cloud storage. The top-tier Ultra plan drops from $250 to $200 per month, with a 20x higher usage limit and access to the full suite of capabilities.
Both tiers include Gemini Spark, the Daily Brief agent — a proactive morning digest that triages email, calendar, and tasks overnight — and access to the new Gemini Omni and 3.5 Flash models. The pricing positions Spark as a premium product — more expensive than Anthropic’s Claude Pro at $20 per month, but comparable to the higher tiers of competing products like Claude Max ($100–$200/month) and OpenAI’s ChatGPT Pro ($200/month).
The risks are real and multidimensional.
Reliability remains the industry’s greatest challenge. Even the best AI models hallucinate, misinterpret instructions, and make errors that a human would never make. An agent that drafts an email to the wrong person, misreads a spreadsheet figure, or sends a payment to the wrong merchant could create consequences that are difficult to reverse. Google’s approach of requiring explicit approval for high-stakes actions like spending money or sending emails is a sensible safeguard — but it also limits how autonomous the agent can actually be. An agent that asks for confirmation at every turn isn’t much of an agent at all.
Privacy is another concern. Spark’s ability to synthesize information across a user’s entire Gmail inbox, calendar, documents, and chat history means it has an extraordinarily deep view of a person’s digital life. Google says Spark operates on a fully managed, secure runtime with isolated ephemeral virtual machines, encrypted credentials, and Data Loss Prevention policies. But the concentration of personal context in a single AI system — accessible through natural language — creates a surface area that will attract scrutiny from regulators, privacy advocates, and security researchers.
Market timing is uncertain, too. The consumer appetite for always-on AI agents is unproven at scale. Google says the Gemini app has 900 million monthly users, but it’s unclear how many of those users are ready for the conceptual leap from “ask a question, get an answer” to “delegate a task, trust the outcome.” The history of digital assistants — from Clippy to early Siri to Alexa — is littered with products that promised proactive intelligence and delivered frustration.
And then there is the question of ecosystem lock-in. Spark works best within Google’s own services. While MCP connections to third-party apps will broaden its reach, the initial experience is one of deep Workspace integration. For the billions of people who live inside Google’s ecosystem, this is a natural fit. For those who split their digital lives across Microsoft, Apple, and other platforms, Spark’s utility will be more limited — at least initially.
Woodward acknowledged as much when asked whether Spark would remain confined to the Google ecosystem. “It’s going to be cross-platform in two ways,” he said — through MCP integrations with third-party apps, and through availability on the web, Android, and iOS, with tasks syncing across devices via the cloud.
Google’s bet with Gemini Spark is that the AI industry’s center of gravity is shifting from models that think to systems that act — and that the company best positioned to win that transition is the one with the most comprehensive set of consumer services to act within. It is a bet backed by enormous infrastructure investment. Google expects to spend approximately $180 to $190 billion in capital expenditure this year — roughly six times what it spent in 2022 — much of it on the AI compute required to run agents like Spark at scale for hundreds of millions of users.
The technology, in other words, is arriving. The models are fast enough, the integrations deep enough, the payment rails secure enough. Google has built a system that can draft your emails, organize your calendar, monitor your inbox, and soon enough, spend your money — all while you sleep.
But the hardest problem in artificial intelligence has never been making a machine capable. It has been making a human comfortable. For two decades, Google’s core promise has been ten blue links and a search box — a transaction built on the assumption that the user is in control. Gemini Spark asks users to renegotiate that relationship entirely, to hand a set of keys to a system that is brilliant, tireless, and still, by its maker’s own admission, best compared to a teenager with a debit card.
Gemini Spark rolls out to trusted testers this week, with a broader beta for U.S. Google AI Ultra subscribers expected next week.