Research
Enterprise Architecture Papers for AI, Cloud, and Data Platforms
Architecture guidance for teams designing systems that must connect AI, data, cloud infrastructure, security, and real operational workflows.
Why architecture still matters
AI can create fast prototypes, but enterprise systems need more than speed. They need durable architecture: clean integration boundaries, ownership, data contracts, security controls, observability, and a way to evolve without breaking critical workflows.
Architecture papers help teams make decisions before tools multiply. They clarify which systems own data, where AI is allowed to act, how users approve actions, and how the organization measures reliability and value.
Core reference patterns
Mativyx architecture work often covers RAG reference designs, event-driven integration, cloud landing zones, API platforms, identity foundations, workflow orchestration, and analytics layers. The goal is to give teams a common blueprint they can implement incrementally.
- AI architecture: models, retrieval, tools, permissions, evaluation, and approval workflows.
- Data architecture: lakehouse, warehouses, pipelines, metadata, quality, and governance.
- Cloud architecture: landing zones, networks, resilience, observability, and cost controls.
- Security architecture: identity, access, monitoring, data protection, and response playbooks.
How teams use the papers
A good architecture paper does not sit in a folder. It becomes a decision record, implementation guide, backlog input, and onboarding document. It helps leadership understand tradeoffs and helps engineering teams build consistently.