Vitea Pulse
Continuous AI Validation. No Drift Goes Undetected.
Test AI before it goes live. And keep testing it after, so drift, hallucinations, and silent vendor changes never compromise patient care.
100+ Healthcare Attack Scenarios
Continuous Drift Detection
Zero Blind Spots Post-Deployment
Partnering to confidently advance healthcare AI innovation.
PROBLEM FRAMING
The AI you approved isn't the same AI running today.
Models change. Performance slips. New vulnerabilities emerge. Without continuous validation, the first warning may be a patient safety incident.
Silent
Vendors change the underlying model behind a tool without always disclosing it — the AI you approved in January may not be the AI running in June.
Degrading
Model performance isn't static. Accuracy today doesn't guarantee accuracy in six months — and most health systems have no baseline to measure against.
Unproven
Vendor performance claims are rarely tested independently. Without a real benchmark, procurement decisions run on sales pitches, not evidence.
Exploitable
AI is a new attack surface. Prompt injection, jailbreaks, and adversarial manipulation target clinical and patient-facing tools the same way malware targets a network.
HOW VITEA HELPS
Prove AI Performance Beyond Go-Live
Vitea Pulse continuously tests every AI application for performance, drift, and security—before deployment and every day after.
Outcomes
Confidence You Can Prove
Continuous validation that turns "we tested it once" into ongoing, documented proof — for patient safety, procurement, and compliance.
100+
healthcare-specific attack scenarios tested
Continuous
post-deployment performance monitoring
NIST AI RMF
aligned compliance reporting
Use case
Prove AI Performance Beyond Go-Live
Continuously validate AI performance, catch drift, strengthen procurement, and generate compliance-ready evidence.
Verify AI performance continuously.
Confirm deployed AI still performs as expected with ongoing testing against established baselines.
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Continuous post-deployment testing
Performance baselines, not one-time checks
Ongoing verification after launch
Catch vendor model changes before patients do.
Detect silent vendor updates and declining accuracy before they affect clinical decisions or patient care.
Real-time drift and accuracy detection
Visibility into unannounced vendor model changes
Early warning before performance degrades
Choose AI vendors on evidence, not promises.
Compare AI vendors head-to-head using auditable performance data—not sales claims.
Head-to-head vendor bakeoffs
Auditable performance baselines
Data-backed procurement decisions
Turn security testing into compliance evidence.
Convert security and performance testing into NIST AI RMF-aligned documentation for leadership, auditors, and regulators.
100+ healthcare-specific attack scenarios
NIST AI RMF-aligned scorecards
Audit documentation, generated automatically
INDUSTRY WARNING
When AI Performance Quietly Fails
“Missed 2 out of 3 sepsis cases after go-live.”
A major health system’s sepsis model degraded after deployment — missing two out of three sepsis cases while generating 109 alerts for every true positive. Continuous benchmarking is built to surface performance drift before it threatens patient safety.
2/3
sepsis cases missed
109 alerts per true positive
Every vendor tells you their AI is secure. Pulse is the first time we've had an independent red team actually test that claim against attacks specific to healthcare.
CISO
Multi-Site Health System
Before Pulse, we had no way to know if a vendor changed their model between our evaluation and go-live. Now we have a baseline, and we know the moment something drifts from it.
CIO
Regional Health System
When our board asks how we're monitoring AI performance after deployment, we used to have an honest answer: we weren't. Pulse gave us continuous testing and a report we can actually hand to auditors.
Chief Compliance Officer
Academic Medical Center
We approved a clinical AI tool based on strong pilot performance. Pulse is what tells us it's still performing that way months later versus just on the day we signed off.
CMIO
Community Health System
We were comparing three AI vendors based entirely on their own performance claims. Pulse gave us a real, apples-to-apples benchmark — the first vendor decision we've made on data instead of a demo.
VP of IT
Multi-Site Health System
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FAQ’S
Got Questions? We've Got Answers.
Let us help you understand and break down the complexity of AI governance.
What is AI model drift, and why does it matter in healthcare?
AI model drift is when an AI system's performance degrades over time as real-world data changes: patient populations shift, lab reference ranges update, or documentation habits evolve. In healthcare, undetected drift can mean an AI tool that was 92% accurate at launch missing critical cases months later, with no alert that anything changed.
How often should healthcare AI models be monitored?
Continuously. Drift can appear within weeks in fast-changing clinical environments, and healthcare regulators increasingly expect ongoing monitoring rather than a one-time validation. Point-in-time testing at go-live tells you the model worked on day one; it says nothing about whether it still works six months later.
How do I know if my AI vendor changed their underlying model?
Most vendors update models through routine releases that aren't always disclosed in detail, meaning the AI you validated may not be the AI running today. Continuous benchmarking against a fixed performance baseline is the only reliable way to catch a silent model change. Waiting for vendor release notes isn't a monitoring strategy.
What is AI red teaming, and does it apply to healthcare chatbots?
AI red teaming is adversarial testing designed to find an AI system's vulnerabilities — including prompt injection, jailbreaks, hallucination triggers — before real attackers or real patients do. For healthcare specifically, this means testing patient-facing chatbots and clinical copilots against attacks built for medical contexts, rather than generic enterprise scenarios.
How does AI hallucination monitoring work?
AI hallucination monitoring evaluates whether an AI's output is actually grounded in the data it was given, rather than fabricated. In clinical settings, this often uses relevance scoring against source documents (like RAG-based systems) to flag responses that sound confident but aren't supported by the underlying chart or clinical data.
What does NIST AI RMF compliance look like for AI monitoring?
The NIST AI Risk Management Framework expects organizations to document ongoing testing, drift detection, and governance. For healthcare organizations, that typically means maintaining auditable performance baselines, tracking model changes over time, and producing reports that map testing results directly to RMF categories.
How is Vitea Pulse different from a one-time AI vendor evaluation?
Vitea Pulse tests every AI application before deployment and continuously afterward, catching drift, hallucinations, and silent vendor model changes as they happen, not just validating performance once at go-live. Most vendor evaluations end at procurement; Vitea Pulse treats validation as ongoing, for as long as the tool stays in use.
Does Vitea Pulse test AI vendors?
Yes. Vitea Pulse runs head-to-head vendor benchmarking and bakeoffs using your real use cases, producing auditable performance records so vendor selection is based on evidence rather than a sales demo, and you have a documented baseline to measure future performance against.
What healthcare-specific attacks does Vitea Pulse test for?
Vitea Pulse runs 100+ healthcare-specific adversarial scenarios, including PHI exfiltration attempts, clinical jailbreaks, and hallucination triggers built around real clinical workflows, not generic enterprise attack libraries repurposed for healthcare.
SCHEDULE YOUR DEMO
See what's really running on your network.
See how Vitea helps healthcare organizations monitor, govern, and reduce AI risk.
Built for healthcare
Purpose-built for healthcare compliance and AI governance.
Reduce AI risk
Identify, monitor, and mitigate AI risks across your organization.
Enterprise-grade security
HIPAA-ready, SOC 2 compliant, and built with enterprise security at the core.
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