Enterprise AI Strategy & LLM Pipelines

The Problem

A UAE management consultancy wanted to move beyond AI as a buzzword and into AI as operational infrastructure. They needed someone to work with C-suite leadership to identify high-impact opportunities, build the systems, and establish governance frameworks to run them responsibly.

The Approach

I led the entire AI function — strategy alignment with executive stakeholders through production deployment. Identified which problems were solvable with current AI (and pushed back on the ones that weren't), then built document intelligence pipelines, recommendation engines, and automation workflows.

Key Technical Decisions

• 01RAG architecture with vector search for document intelligence across corporate knowledge bases
• 02Structured LLM pipelines with validation layers — not raw API calls with fingers crossed
• 03MLOps monitoring and governance frameworks for model lifecycle management
• 04Phased rollout: high-confidence automation first, human-in-the-loop for ambiguous cases

The Outcome

30%+ reduction in manual operational work through targeted automation. The organization moved from 'we should use AI' to 'we have AI systems in production with governance and monitoring.' Scalable LLM pipelines handling real business documents.