Dawoud Al-MughrabiProduction AI
Architecture

The patterns behind production AI systems.

The architecture patterns behind my AI work: private LLMs, RAG, decision intelligence, data pipelines, governed deployment, and reliability evaluation.

Private LLM / RAG Architecture

Private LLM / RAG Architecture flow
  1. 01Documents
  2. 02Ingestion
  3. 03Parsing
  4. 04Chunking
  5. 05Embeddings
  6. 06Vector Store
  7. 07Retrieval
  8. 08Context Assembly
  9. 09LLM
  10. 010Source-Grounded Answer
  11. 011User Feedback

The quality of a RAG system comes from chunking, metadata, retrieval filters, source citations, local model constraints, hallucination controls, and feedback loops. The model is only one piece of the system.

Decision Intelligence Architecture

Decision Intelligence Architecture flow
  1. 01Operational Data
  2. 02Validation
  3. 03Constraint Layer
  4. 04Rules / Scoring / Optimization
  5. 05Recommendation Engine
  6. 06User Workflow
  7. 07Decision Trace
  8. 08Outcome Capture

Decision systems need constraints, recommendation logic, human-in-the-loop design, audit trails, and scenario comparison. The goal is better decisions, not invisible automation.

Deployment Architecture

Deployment Architecture flow
  1. 01Google Compute Engine VPS
  2. 02Nginx reverse proxy
  3. 03Docker Compose
  4. 04Next.js standalone container
  5. 05Static-first MDX content

Prior systems used patterns like FastAPI, React, Postgres, workers, Docker, Nginx, SSO/RBAC, and monitoring. This portfolio uses a simpler production container: Next.js standalone output behind host-level Nginx.

Data Pipeline Architecture

Useful enterprise AI often starts with disciplined ingestion: CSV validation, file hashes, incremental loads, metadata extraction, data-quality checks, stale-value detection, repeated-value detection, and batch processing that can be replayed.

Security & Governance Architecture

Governance appears in SSO, RBAC, department-aware access, audit logs, data boundaries, safe mode, rollback, review gates, and approval states. It should be part of the system users touch.

Evaluation & Reliability Architecture

Production AI evaluation includes RAG answer grounding, retrieval relevance, citation behavior, human review loops, confidence/severity separation, safety-sensitive summarization, false positive and false negative handling, latency, fallbacks, monitoring, logging, rollback, and safe-mode thinking.