From Pilots to Practice: Inside Corti's Invisible Infrastructure for Clinical-Grade Healthcare AI

The Invisible Infrastructure: Corti's Andreas Cleve on AI in Frontline Care

September 24, 2025

Corti now supports around 250,000 patient interactions every day across multiple countries. That volume signals a clear appetite for AI in care delivery. It also exposes the gap: clinicians want relief from administrative grind, yet trust lags when tools miss clinical nuance or impose extra work.

What scale tells us about readiness

• Demand is real: 80%+ of European clinicians want AI to ease workload, but only a fraction trust current tools.
• Pilots stall: far too many projects never reach production because they weren't built for clinical contexts.
• The fix is infrastructure: healthcare-tuned systems that understand medical language, workflows, compliance, and context from day one.

Once clinicians use AI that actually fits their workflow and standards, trust follows. With billions still lacking access to care, scaling safely is not the finish line - it's the entry point to closing the gap.

Why general-purpose models fall short in care settings

Healthcare isn't a generic task. It's specialised, regulated, and unforgiving to errors. General models can be fun and helpful for casual tasks; clinical work demands precision, auditability, and continuous alignment with medical standards.

Clinical-grade infrastructure focuses on thousands of niches - specialties, abbreviations, documentation patterns, coding systems, and reasoning steps unique to medicine. It's less about size and more about the right capability applied in the right context.

Built for Europe and the U.S., without choosing speed or safety

Different regions emphasise different priorities. Europe leans toward stewardship and patient protection. The U.S. values speed, liability coverage, and ROI. Corti operates in both by baking in compliance, auditability, and deployment flexibility from day one.

• GDPR and HIPAA alignment with audit trails and data controls
• Sovereign cloud options for data residency and governance
• Clinical alignment that scales across sites and systems

For reference: GDPR overview and HIPAA summary.

Make AI invisible at the point of care

The first rule: don't add to the burden. Many clinicians still spend hours each week correcting outputs they can't trust. That's the opposite of progress.

The answer is handling complexity at the infrastructure layer - interoperability, compliance, and auditability "industrialised" so developers and providers ship tools that work on day one. Adoption happens when AI gives clinicians time back, not more to fix.

Capital allocation: deepen the foundations

With over $100 million raised, the priority is clear: strengthen the core infrastructure. That means a more reliable API, more healthcare-specific models, and removing the paperwork nobody went to medical school to do.

On the horizon: agentic capabilities and automation of complex workflows. The job now is to make them safe, compliant, and usable so teams can deploy them in real clinical settings - without chasing every shiny feature.

Trust, safety, and transparency by design

In care, even a 1% error rate costs lives. Trust isn't a promise; it's engineered. Corti's models are trained on millions of hours of clinical dialogue, validated in peer-reviewed studies, and stress-tested in live deployments.

Every interaction carries an audit trail, real-time quality checks, sovereign cloud options, and alignment with medical standards. Hospitals get evidence and safeguards, clinicians gain confidence at the bedside, and patients see AI held to medical-grade accountability.

The next 5-10 years: the tech you don't notice

Success looks invisible. Like electricity or the internet, infrastructure fades into the background when it works. In five years, patients won't ask, "Is AI in the room?" They'll just see shorter waits, faster documentation, and more time with their clinician.

In ten years, the most advanced systems will quietly handle compliance, documentation, reasoning support, and interoperability. Human caregivers can focus on empathy and judgment - the reasons they chose medicine. As this infrastructure reaches underserved areas, safe and trusted care scales to billions.

Practical steps for healthcare leaders

• Set a high bar: require peer-reviewed validation, bias testing, and prospective performance monitoring.
• Demand auditability: full logs, versioning, and explainability for clinical decisions and documentation.
• Protect data: insist on GDPR/HIPAA alignment, minimisation, encryption, and sovereign options where needed.
• Fit the workflow: EHR integration, ambient capture that reduces clicks, and zero extra steps at the bedside.
• Measure outcomes: track time saved, documentation quality, guideline adherence, and patient experience.
• Pilot to scale: design pilots with clear success criteria, then standardise rollout playbooks and training.

Bottom line

Healthcare won't benefit from generic AI bolted onto clinical work. It needs clinical-grade infrastructure that understands the language of care, respects regulation, and proves itself in practice. Make the foundation strong, and the experience becomes simple for everyone involved.

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