Engineer-citable reference architectures from iSimplifyMe. Each paper documents a production pattern we deploy for clients — private LLM, AWS Bedrock, regulated-industry posture, and the engineering tradeoffs that shape model selection, isolation, and compliance.
A reference architecture for controlling production AI cost on AWS Bedrock — prompt caching, per-task model routing, cache-aware routing, cheaper defaults, and spend observability: the cost layer that holds spend flat as usage scales across an organization.
A reference architecture for deploying production AI inside HIPAA-regulated workflows, drawn from our work building healthcare AI infrastructure on AWS Bedrock and SageMaker.
A reference architecture for the data and retrieval layer of LLM-native AI systems on AWS Bedrock — pipelines, permissioned retrieval, hybrid search, context engineering, memory, and feedback loops — drawn from iSimplifyMe production deployments in regulated and mid-market work.
A reference architecture for the reliability layer of LLM-native systems on AWS Bedrock — layered guardrails, atomic content integrity, investigate-only audit agents, circuit breakers, retries, and quality gates — the engineering that decides whether a deployed AI system holds up in regulated production or decays into a demo.
A reference architecture for the business-integration layer of an LLM-native platform on AWS — single-table multi-tenancy with isolation by construction, domain-routed tenant resolution, a unified lead pipeline, role-permissioned dashboards, and synchronized billing — the layer that turns AI capability into a product many clients run on one platform.
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