Skovos vs Ferrum Health
Skovos publishes this comparison. Ferrum Health is described from its own public website, cited below. Ferrum is a respected clinical AI company, and the two products solve different problems.
What is the difference between Skovos and Ferrum Health?
Ferrum Health helps health systems deploy and monitor clinical AI models, especially in imaging and specialty care, through a curated model hub, observability and a deployment layer. Skovos governs AI agents that take actions, by giving each agent an identity, checking every action against permissions, keeping a tamper-evident audit trail, and letting the hospital stop an agent immediately. Ferrum is centered on models and their performance. Skovos is centered on agents and their authority.
What Ferrum Health does well
Ferrum sells an AI Governance Suite with three parts:
- Model Hub: more than 60 validated models across service lines such as cardiology, neurology, oncology, thoracic, musculoskeletal and women's health, with, in Ferrum's words, "no vendor lock-in."
- Observability Lens: continuous performance, safety and ROI tracking, with drift "caught the moment it appears."
- Deployment Fabric: one connection into cloud, on-prem and existing workflows, including Epic and PACS.
Ferrum states that PHI stays in the customer's environment of choice and cites a 12-week contract-to-deployment timeline. Its site names Sutter Health, Allina Health, Carle Health, Premier and Lumexa Imaging among its customers.
For a radiology or specialty department that wants to bring many validated models into production and watch their accuracy over time, Ferrum is a strong, proven option.
What Skovos does
Skovos is the governance layer for AI agents in health systems. Agents differ from classic clinical models because they call tools: they read and write records, send messages and place requests. Skovos governs that behavior:
- Registered identity for each agent, with an accountable owner and scoped permissions.
- Permission checks on every action, with each allow or deny decision audited.
- A hash-chained audit trail the hospital owns and can verify for tampering.
- A recall control that stops an agent at once. After recall, every later check denies it.
- Signed evidence exports in an OVERT v1.1 style profile for auditors.
Skovos governs agents that are connected to it or checked through it. It does not measure model accuracy or detect imaging drift, and it does not host clinical models.
Side-by-side comparison
| Ferrum Health | Skovos | |
|---|---|---|
| Center of gravity | Clinical AI models | AI agents that take actions |
| Model catalog | 60+ validated models | None; governs what you already run |
| Performance and drift monitoring | Yes, Observability Lens | No; pair with a monitoring tool |
| Deployment infrastructure | Yes, cloud or on-prem | No |
| Per-action permission checks | Not described on its site | Yes |
| Hospital-owned stop control for agents | Not described on its site | Yes, recall |
| Tamper-evident audit | Not described on its site | Hash-chained, with integrity check |
| Signed evidence export | Not described on its site | Yes |
| Named health system customers | Yes | None listed |
"Not described" means we did not find it on Ferrum's public site. Ask Ferrum directly; its product may do more than its home page says.
When Ferrum Health is the better fit
Choose Ferrum when your priority is clinical models: getting validated imaging and specialty AI into production quickly, keeping PHI in your environment, and tracking accuracy, drift and ROI per model. Its model hub also saves the work of contracting with dozens of individual AI vendors.
When Skovos is the better fit
Choose Skovos when your priority is agents: ambient scribes that draft orders, prior authorization agents that submit to payers, patient messaging agents, or in-house agents built on Claude or ChatGPT. These systems need to be told what they may touch, checked every time, and stopped by the hospital when needed. That is Skovos's job.
Using both
The two can work side by side. Ferrum can deploy and watch imaging models. Skovos can register the agents that consume those models' outputs and act on them, check each action and keep one audit trail. A drift alert from a monitoring tool can be the trigger for a Skovos recall.
Questions to ask in a pilot
- Which AI systems will this product see, and which will it miss?
- Show me how a performance problem is detected and who is alerted.
- Show me how an agent is stopped, and how fast the stop takes effect.
- What record does each check or stop produce, and who owns it?
- Can an outside auditor verify that record without the vendor's help?
Frequently asked questions
Is Ferrum Health an AI governance platform?
Yes. Ferrum calls its product an AI Governance Suite, focused on deploying, validating and monitoring clinical AI models.
Does Skovos monitor model drift?
No. Skovos governs what agents are allowed to do and records what they did. Use a monitoring tool such as Ferrum, Fiddler or Arize for drift.
Which is better for imaging AI?
Ferrum, because its model hub and deployment layer are built for imaging and specialty models. Skovos is designed for agents that take actions.
Can Skovos stop a model hosted by another vendor?
Skovos can stop any agent that checks its permissions through Skovos. If a system is not connected to Skovos, Skovos cannot see or stop it.
Do I need both?
Many health systems will. Model performance and agent authority are different risks, and one tool rarely covers both well.
Related reading
- AI model drift detection in healthcare
- AI safety monitoring for health systems
- AI agent inventory for health systems
- How health systems evaluate enterprise AI platforms
Sources
- Ferrum Health home page (AI Governance Suite, Model Hub, Observability Lens, Deployment Fabric, customers): https://ferrumhealth.com/
- Skovos connector documentation: https://mcp.skovos.ai/docs
- Skovos overview: https://www.skovos.ai/llms.txt