AI Architecture
The architecture behind intelligent enterprises.
A reference model for enterprise AI platforms, the capabilities it serves, the way it gets delivered and the principles that hold it together. Explore it layer by layer.
02AI Architecture
How I think about enterprise AI architecture.
Explore the architecture
Pick a capability to see where it lives in the stack, or open any layer.
RAG
Grounding models in enterprise knowledge
Architected RAG pipelines combining vector databases, semantic search and knowledge graphs so model output is grounded in enterprise context.
- Components
- LLMsRAGSemantic SearchVector DatabasesQdrantKnowledge Graphs
- Applied at
- Tulasea Inc.
Where people meet AI: web and mobile channels, copilots, conversational interfaces and the enterprise applications teams already use.
ExperienceMicrosoft Copilot adoption and conversational AI across 1000+ users at Comcast; conversational AI systems within the Tulasea platform.
Coordinates reasoning and action: agents, agentic workflows, prompts and the tool calls that let AI act on enterprise systems safely.
ExperienceWorkflow orchestration and AI-assisted workflows in the Tulasea platform; intelligent automation initiatives at Comcast.
The models and retrieval that produce answers, predictions and recommendations, grounded in enterprise knowledge.
ExperienceLLMs, RAG pipelines and semantic search at Tulasea; recommendation engines, fraud and anomaly detection and risk scoring at Comcast.
Enterprise knowledge made usable: vectors for similarity, graphs for relationships, and data platforms for scale.
ExperienceGraph-driven intelligence with JanusGraph and Qdrant at Tulasea; Databricks and Apache Spark for high-volume data at Comcast.
Connects AI to the business: APIs, event streams and microservices bridging CRM, ERP, EHR and operational systems.
ExperienceSecure API orchestration with EHR and operational systems at Tulasea; Kafka-based integration of CRM and OMS at Comcast; ERP and CRM integration earlier in her career.
Cloud-native infrastructure that keeps AI workloads scalable, resilient and repeatable to deliver.
ExperienceAWS and Azure at Tulasea; Kubernetes, Docker and CI/CD for enterprise AI workloads at Comcast.
Identity, access, oversight and accountability applied across every layer above, not bolted on at the end.
ExperienceLed AI governance architecture at Tulasea: authentication, authorization, explainability, observability, auditability, privacy and compliance.
Requests flow down, context and responses flow back up. Trust and governance apply to every layer.
01What I architect
Four areas where AI meets enterprise reality.
- 01
Enterprise AI Platforms
Enterprise AI ecosystems that bring LLMs, RAG, enterprise data, APIs and secure cloud services together as one platform.
- LLM & RAG architecture
- Vector search & knowledge graphs
- Cloud-native services
- 02
Agentic AI Systems
Agent workflows with reasoning, access to enterprise tools, orchestration, governance and a human in the loop where it matters.
- Agent orchestration
- Tool calling & workflows
- Human oversight
- 03
Enterprise Integration
Connecting AI platforms to enterprise applications through secure APIs, event-driven architecture, microservices and proven integration patterns.
- API architecture
- Kafka event streaming
- CRM, OMS & EHR integration
- 04
AI Governance & Security
Identity, authorization, explainability, observability and auditability built into AI platforms as architecture, not afterthoughts.
- OAuth2 / OIDC, RBAC / ABAC
- Explainability & audit
- Privacy & compliance
03Approach
From business problem to AI platform.
Understand
Start from the business, not the model.
- Business goals
- Users
- Processes
- Enterprise constraints
Architect
Shape the target state across every layer.
- Solution architecture
- AI architecture
- Data architecture
- Integration architecture
Design for Trust
Build governance into the design.
- Security
- Governance
- Privacy
- Explainability
Engineer for Scale
Make it buildable and resilient.
- APIs
- Microservices
- Cloud
- Data
- Infrastructure
Operationalize
Run it, measure it, improve it.
- Monitoring
- Observability
- CI/CD
- Audit
- Optimization
07Architecture principles
Six principles behind every design.
- 01
Business-Aligned
Architecture starts from enterprise strategy and the outcome the business needs. Technology choices follow.
- 02
Secure by Design
Identity, authorization and data protection are part of the first design, not a review at the end.
- 03
API First
Capabilities are exposed through well-defined, secured APIs so AI can reach enterprise systems and systems can reach AI.
- 04
AI With Governance
Explainability, observability and auditability make AI decisions traceable and accountable.
- 05
Cloud Native
Containerized microservices and automated delivery keep platforms portable, resilient and repeatable.
- 06
Designed for Scale
Event-driven, distributed patterns let platforms grow in users, data and use cases without redesign.
Contact
Let's architect what comes next.
Interested in enterprise AI platforms, Generative AI architecture, agentic systems or digital transformation? I'd be glad to hear what you are building.
- Phone
- +971 54 491 5339