Custom AI foundries are an emerging architecture for building and reusing healthcare AI without replacing a hospital’s electronic records, operational systems, or accountability structures. The idea is to curate governed clinical data, make multiple models and agents available through reusable services, and connect them to existing workflows through interoperability and orchestration layers.
“Decoupling” does not mean moving clinical AI away from the hospital’s governance. It means separating experimentation and reusable technical components from legacy-system constraints while keeping data rights, clinical oversight, workflow ownership, and institutional responsibility intact.
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What a custom AI foundry is—and is not
A custom AI foundry is best understood as a strategic design pattern, not a universally defined product category. McKinsey’s November 2025 analysis describes a possible move from disconnected healthcare AI point solutions toward a modular architecture that links point applications, data infrastructure, and intelligent agents. Its “clinical-data foundry” concept centers on curating governed clinical information so that it can support more than one model or use case.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThat makes a foundry different from a simple cloud migration, data lake, or chatbot deployment. A cloud can provide compute and storage, but it does not by itself establish who may use a record, which model version produced an output, how an output enters a clinician’s workflow, or who can override it. A foundry supplies reusable data, model, integration, and control layers around those decisions.
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The hospital’s existing electronic health record, imaging systems, laboratory systems, identity controls, and administrative applications remain part of the environment. New AI capabilities connect to them through defined interfaces rather than requiring the institution to discard them. Innovation is decoupled at the architecture level; clinical accountability is not.
Why hospitals are considering a modular approach
Point solutions can solve an immediate operational or clinical problem, but each additional tool may introduce another data feed, authentication path, model contract, monitoring process, and workflow change. Over time, a collection of individually useful systems can create duplicated integrations and inconsistent information.
A foundry treats those repeated capabilities as shared infrastructure. A governed clinical-data layer can prepare information for several applications. A model-access layer can expose internally developed models, external models, or agents through consistent controls. An integration layer can connect those capabilities to records and other systems once, instead of creating a separate custom connection for every application.
McKinsey’s authors—Aneesh Krishna, David Friend, Neeraj Gohad, and Prashanth Reddy—summarize the strategic issue this way: “The real battleground will be who controls the data and orchestration layers.” That is an argument about where durable advantage and risk may accumulate, not a claim that a universal foundry standard already exists.
The architecture in plain terms
A practical foundry has several connected layers. They can be supplied by one organization, assembled from multiple products, or built internally. The important property is that each layer has a clear contract with the others.
| Layer | What it does | Questions leaders should answer |
|---|---|---|
| Governed data foundation | Collects, curates, catalogs, and serves clinical and operational data for approved purposes. | Who has rights to each dataset? How is quality measured? What sharing policy applies? |
| Model and agent access | Hosts or connects to models and agents that can be reused across applications. | Which models are approved? Can the organization combine its own models with outside providers? |
| Interoperability and integration | Translates and routes information between AI services, electronic records, and other health-system systems. | Which standards and APIs are supported? How are identity, consent, and error handling managed? |
| Orchestration and monitoring | Chooses when a model runs, supplies context, records outputs, and monitors performance and risk. | What triggers an invocation? Where is the audit trail? Who receives an alert when performance changes? |
| Workflow applications | Presents recommendations or automation inside clinical and administrative processes. | Where does a qualified person review, accept, edit, or reject the result? |
Governance crosses every layer. Data policies cannot be added after deployment, because the way data is collected and transformed determines which models can legally and safely use it. Likewise, a model registry, ownership record, version history, and retirement process belong in the platform design rather than in a spreadsheet maintained after the fact.
How to build a foundry without destabilizing existing systems
- Start with a high-value, bounded use case. Select a clinical or administrative workflow with a measurable problem, an accountable owner, and data that can legally be used. McKinsey recommends proving value before expanding rather than attempting an enterprise-wide replacement.
- Map the information and its rights. Identify source systems, data custodians, permitted uses, retention rules, patient or partner restrictions, and the people responsible for approving access. Include derived data and prompts, not only the original record.
- Create a catalog and sharing policy. Record definitions, provenance, quality checks, update frequency, and approved consumers. A catalog makes it possible to discover reusable data without treating every project as a new extraction exercise.
- Establish a model registry. Give every model or agent an owner, version, intended use, evaluation record, dependencies, and retirement path. Record which data and instructions produced each production result.
- Add an interoperability layer. Connect the pilot to the systems where work already happens. Define authentication, authorization, terminology mapping, timeout behavior, duplicate handling, and what happens when a source system is unavailable.
- Orchestrate one workflow end to end. Specify the trigger, context supplied to the model, output format, review step, escalation path, and storage of the final decision. Keep a human accountable for actions that affect care or operations.
- Monitor before scaling. Track technical reliability, data drift, model performance, overrides, delays, and safety incidents. Monitoring should include the workflow outcome, not merely whether an API returned a response.
- Turn the pilot into a reusable service. If the use case demonstrates value, generalize the data contract, model interface, and audit controls so another approved workflow can use them. Point tools can serve as on-ramps to broader redesign, but they should not become permanent exceptions to governance.
How architecture options differ
There is no evidence of a single winning implementation. The following examples illustrate different design choices; the capabilities are descriptions made by the vendors themselves and should be verified in procurement and technical diligence.
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| Decision area | Architecture example described by the source | What to verify before adoption |
|---|---|---|
| Data control and location | Bavel Health describes a sovereign clinical-data platform that operates inside hospital environments and uses a federated learning layer in which AI workloads move to data. | Confirm tenancy, network boundaries, administrator access, export controls, and how federated updates are protected and audited. |
| Deployment model | Coforix describes SaaS and customer-cloud deployment options. | Establish which party operates each component, where logs and backups reside, and how the organization can exit or migrate. |
| Interoperability | Coforix lists FHIR, SNOMED CT, and ICD-10 and describes a single-API approach. | Test real interfaces, terminology mappings, write-back behavior, latency, and support for local extensions rather than relying on a standards list. |
| Model and vendor choice | Coforix describes an open, multi-vendor design; Bavel describes a platform that can support federated learning and a clinical AI fabric. | Check whether internal models, external models, and agents can be combined without hidden exclusivity, and inspect switching costs. |
| Workflow integration and governance | deepc describes a single integration and governance foundation for AI workflows across health systems; Coforix also positions its layer as connecting models to hospital workflows. | Require evidence of role-based controls, approval paths, audit records, incident handling, and human review in the exact workflow being purchased. |
What the named examples demonstrate
McKinsey’s modular architecture thesis
McKinsey’s November 2025 article is the clearest statement of the foundry idea in the supplied material. It argues that proliferation of point solutions can produce fragmentation and that organizations should prepare for a connected, modular architecture with governed clinical-data foundries. The article offers strategic recommendations—high-value pilots, catalogs, sharing policies, model registries, traceability, and multidisciplinary governance—not a technical standard or proof that every health system can implement the pattern in the same way.
GE HealthCare and AWS
In a July 25, 2024 announcement, GE HealthCare said it had selected AWS as a strategic cloud provider and planned to use AWS services to develop healthcare foundation models and generative AI applications. This is evidence of a partnership strategy and intended direction. It is not evidence that every announced plan had been completed or that clinical benefits had been independently demonstrated. The announcement also discussed a research-stage imaging tool and stated that it was in development and not cleared or approved for commercial availability.
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Bavel Health
Bavel Health describes a platform combining data infrastructure, a virtual research room, federated learning, and a clinical AI fabric. Its description says the platform operates inside hospital environments and that workloads move to the data. Those statements show one way to pursue institutional data sovereignty; they are company descriptions, not independent verification of security, performance, or clinical outcomes.
Coforix
Coforix describes an integration platform for models and agents across hospitals and vendors. Its platform page references FHIR, SNOMED CT, ICD-10, a single API, SaaS or customer-cloud deployment, and a statement that patient data is not used to train its models. Each point should be treated as a vendor claim to be tested contractually and technically, especially the training-data policy and the practical behavior of the API.
deepc
deepc positions its product as a single integration and governance foundation for clinical AI workflows across health systems. It is a useful example of the infrastructure-and-orchestration category. The cited material does not establish independent performance results, so a buyer would still need workflow-specific validation and monitoring requirements.
Governance is part of the infrastructure
Data rights and sharing
Define which institution, partner, or patient rights holder permits each use. A data-sharing policy should cover collection, transformation, secondary use, cross-organizational learning, retention, deletion, and downstream products. Federated deployment can reduce movement of raw data, but it does not eliminate the need to govern who may compute on it and what leaves the environment.
Ownership and traceability
Assign an owner to every dataset, model, agent, prompt template, and production workflow. Preserve lineage from source record to transformed input and output. A registry should show versions, approvals, evaluation results, dependencies, and retirement decisions so that an incident can be investigated without reconstructing history manually.
Risk controls and human oversight
Classify use cases by consequence and require stronger controls where an incorrect output could affect care, access, billing, or safety. Human review must be designed into the workflow, with enough context to challenge a recommendation and a clear escalation route. Logging an approval button is not equivalent to meaningful oversight if staff cannot inspect or override the result.
Performance and change management
Monitor data quality, drift, latency, failure rates, overrides, and outcome measures after deployment. Set thresholds that pause or roll back a model. When a model, data source, terminology map, or workflow changes, repeat the relevant evaluation instead of assuming that an earlier approval still applies.
Procurement questions that expose weak foundry proposals
- Can the supplier show the complete data-flow diagram, including logs, backups, support access, and model-training paths?
- Which standards are implemented in production, and which are merely supported in documentation?
- Can the health system run an internal model beside external models and agents, and remove one without rewriting every workflow?
- Where are model cards, evaluation records, version histories, audit logs, and incident reports stored?
- What happens when a source system is down, data is incomplete, a model times out, or two systems disagree?
- How are clinicians or administrators notified, trained, and protected from automation bias?
- Which outcomes will prove value for the pilot, and who has authority to stop expansion?
- What contractual terms govern patient data, derived data, model improvement, retention, portability, and deletion?
Common failure modes and practical fixes
Calling a cloud migration a foundry
Moving records to a cloud may improve elasticity, but it does not create reusable governance or workflow integration. Fix the gap by defining data contracts, a model registry, ownership, and audit requirements before adding more applications.
Starting with a broad transformation program
An enterprise-wide launch can obscure whether any workflow improves. Begin with one bounded, high-value use case, publish its acceptance criteria, and use the resulting interfaces and controls as reusable building blocks.
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Buying isolated tools with no shared layer
Adding another point solution may increase fragmentation if it creates a new interface and account for every department. Require common identity, terminology, logging, and orchestration services, or document why an exception is justified.
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Product pages can accurately describe intended architecture while offering no independent clinical-impact study. Separate capability verification, security review, and workflow testing from claims about benefits, and require evidence appropriate to the use case.
Leaving governance until after deployment
Retrofitting rights, traceability, and human review is expensive and can force a rollback. Make approval, lineage, monitoring, and escalation part of the initial design and contract.
Assuming decoupling means independence from the hospital
An AI service that is disconnected from records, identity, workflow, and accountability is not a safe foundry. Keep integration and institutional responsibility explicit even when experimentation occurs in a separate technical environment.
When the foundry pattern is a good fit
The approach is most useful when an organization expects several approved AI capabilities to share data, model services, terminology, monitoring, and workflow controls. It is also appropriate when the hospital wants to preserve existing systems while creating a repeatable path for new clinical or administrative applications.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA single, low-risk experiment may not justify a full foundry. In that case, the organization can still apply the same principles on a smaller scale: document rights, keep a model owner, integrate through supported interfaces, record outputs, and provide human oversight. The architecture should grow in response to proven reuse rather than become a large platform whose value is assumed.
What “decoupling innovation” means in practice
Custom AI foundries give hospitals a way to separate the pace of model experimentation from the replacement cycle of core infrastructure. Reusable data and integration services can let teams test or deploy new capabilities without rebuilding the electronic record. The trade-off is that the institution must invest in the less visible work—rights management, catalogs, registries, orchestration, monitoring, and accountable workflow design.
The strongest interpretation of the foundry concept is therefore neither “put healthcare AI in the cloud” nor “remove the hospital from the loop.” It is a governed, modular architecture in which innovation can move faster while data control, interoperability, and human responsibility remain explicit.
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