Question 1

Where does AI fail in healthcare deployments?

Accepted Answer

Most commonly at the data layer. The model is fine; the patient record is incomplete, the clinical event timestamps are misaligned, or the payer denial reason codes are inconsistent across years. AI readiness work is mostly data work, not model work.

Question 2

Do you handle FDA AI/ML SaMD readiness?

Accepted Answer

We focus on the data layer that supports clinical AI tools rather than the FDA submission process itself. For SaMD-regulated tools, your medical-device regulatory team handles the FDA submission; we make sure the data and governance posture supports it.

Question 3

What is the typical timeline for a healthcare AI readiness engagement?

Accepted Answer

30-day Analytics Truth Audit ($35,000 fixed) covers the readiness diagnostic. Implementation work to close the readiness gaps typically runs 12 to 24 weeks at $160,000 to $480,000 depending on the workload count.

Question 4

What does AI readiness actually mean?

Accepted Answer

AI readiness means the data foundation is clean enough, the governance is defensible enough, and the metric layer is consistent enough that AI deployment lands without producing the kind of hallucinations or compliance issues that block production use. The work is mostly at the data layer.

Question 5

Do you build AI models or just the data layer?

Accepted Answer

We focus on the data foundation that supports AI deployment. Model selection, training, and tuning we coordinate with the client's AI team or specialist partners. The data layer is where most AI pilots stall.