Model Consultation

Choose the deployment architecture that fits your device, your customers, and your regulatory obligations, not just your engineering preferences.

Overview

Deployment architecture decisions for AI medical devices ripple through regulatory submissions, customer procurement cycles, and operational economics for years. Picking 'cloud-first' because it is easier engineering can disqualify your product from hospital IT review boards. Picking 'on-prem' because it sounds safer can exclude you from health systems migrating to managed services.

We map the choice against your specific product, target customer profile, and regulatory posture, and produce a defensible recommendation with documented trade-offs.

Our Process

  1. 1

    Constraint inventory

    Device class, data residency requirements, latency, integration points, target customer IT posture.

  2. 2

    Architecture candidates

    Cloud, on-prem, edge, hybrid, scored against constraints.

  3. 3

    Regulatory implications

    FDA submission, EU MDR, HIPAA, state data residency rules per option.

  4. 4

    Customer fit analysis

    Will the IT review board at your top 20 target accounts approve this?

  5. 5

    Recommendation memo

    Defensible choice with trade-offs documented.

Frequently Asked Questions

Can you change architecture later?

Yes but expensive. The point of this work is to choose right the first time.

What about FedRAMP / StateRAMP?

Surfaced when target customers require it. We map the timeline cost of certification.

Edge deployment realistic for AI?

Yes, particularly for latency-sensitive, network-fragile, or data-sovereignty constrained settings.

Does this overlap with FDA submission?

Coordinated, architecture decisions feed directly into your 510(k) / De Novo / PMA submission.

Choose the architecture you can defend.

Tell us your product and target customers. We will return an architecture recommendation within four weeks.

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