AI infrastructure under compliance constraints is a different engineering discipline.
Standing up a RAG pipeline against a hosted model is a weekend project. Standing up a RAG pipeline that handles PHI, survives a HIPAA audit, and supports model evaluation, prompt logging, and provenance is engineering work most teams have not had to do yet.
We have built AI infrastructure for healthcare AI teams running clinical inference, AI/SaaS vendors selling into federal and hospital buyers, and platforms running RAG against regulated data. The pattern repeats: the model is fine, the application is fine, and the infrastructure between them was built for proof-of-concept speed and is now blocking a compliance review or a customer onboarding.
Stonebridge builds AI infrastructure with the compliance posture baked into the platform: BAA-eligible endpoints, regulated vector stores, identity-scoped retrieval, signed model artifacts, and evaluation as a first-class part of the platform.
What an AI platform actually needs to include.
The platform surface that survives both regulatory scrutiny and production load includes the following capabilities. Each is shipped as code in every build engagement.
- Training pipelineReproducible, signed training runs with documented data lineage. Datasets versioned. Pipeline runs evidenced and stored under retention locks.
- Model registryVersioned model artifacts with provenance: training run, dataset, evaluation results, signing chain, deployment authorization.
- Serving infrastructureInference endpoints with autoscaling, identity-scoped access, request logging, and the same admission discipline as any other production workload.
- RAG / vector storesVector store inside the regulated boundary when the source data is regulated. Embeddings of PHI/CUI are themselves regulated. Retrieval scoped to identity and logged.
- Evaluation pipelineContinuous offline evaluation against held-out sets. Online evaluation through shadow traffic, A/B, and human-in-the-loop labeling.
- ObservabilityPrompt and response logging with PII scrubbing, latency and cost dashboards, drift detection on production inputs.
From training run to production inference, with provenance throughout.
The standard Stonebridge AI platform threads provenance through every stage. Nothing reaches a production endpoint without a signed lineage that the platform can produce on demand.
Data
- Dataset versioning (DVC / LakeFS / Delta)
- PHI / PII classification
- Lineage manifest per run
- Retention policy enforced
Train
- GPU cluster (EKS / GKE / AKS)
- Distributed training (Kueue / Volcano)
- Hyperparameter tracking (MLflow / W&B)
- Reproducible run manifest
Evaluate
- Held-out + canary sets
- Safety + bias evaluation
- Cost-per-inference tracking
- Human-in-the-loop labeling
Register & Sign
- Model registry with lineage
- Cosign-signed model artifacts
- Deployment authorization chain
- Audit log of promotion events
Serve
- Serving framework (Triton / KServe / vLLM)
- Identity-scoped endpoints
- Request + response logging
- Autoscaling on real-time signal
Monitor
- Latency + cost dashboards
- Drift detection on inputs
- Quality regression alerts
- Continuous evaluation loops
Five patterns that fail compliance and fail production.
We see the same mistakes repeatedly when teams ship AI infrastructure without help. None are about not understanding ML. They are about not understanding what the infrastructure has to do when the workload is regulated.
Embeddings of PHI in an unregulated vector store
Embedding regulated text and sending the vectors to a hosted vector database outside the BAA boundary is a HIPAA finding. Embeddings of regulated data are regulated. Vector stores inside the boundary.
Prompt and response logs with no PII scrubbing
Logging every prompt and response for evaluation, then storing them in a log aggregator the security team has never reviewed. PII scrubbing has to be part of the logging pipeline, not a follow-up task.
No model provenance
A production model with no documented training run, no signed artifact, and no evaluation lineage. The first incident or audit query exposes the gap. Provenance is shipped with the platform, not produced under deadline.
Inference endpoint shared with non-regulated workloads
One serving cluster handling regulated and non-regulated traffic. Logging, IAM, and network architecture have to assume the worst case across both, which fails for both. Separate clusters or separate endpoints.
Treating evaluation as a notebook
Evaluation that lives in a notebook that one engineer runs before deploys is not a system. Continuous evaluation, with versioned eval sets and tracked metrics, is the only way to know whether a model regressed in production.
Two ways to engage. Fixed scope, fixed price.
Most clients start with an audit of their existing AI infrastructure, then move to a build. Teams under a customer or compliance deadline come straight to the build.
AI Infrastructure Audit
Two-week, fixed-fee assessment of your existing AI/ML platform against security, compliance, and reliability baselines. Produces a written report with prioritized remediation roadmap.
- 2 weeks duration
- Training + serving review
- RAG / vector store review
- Compliance mapping (HIPAA / FedRAMP / SOC 2)
- Prioritized remediation roadmap
AI Platform Build
Ten-week hands-on engagement to architect and ship a production AI platform. Training, registry, serving, RAG, evaluation, and observability under your compliance posture from day one.
- 10 weeks duration
- Reproducible training pipeline
- Model registry with provenance
- Serving infrastructure
- RAG / vector store inside the boundary
- Continuous evaluation pipeline