8 Pillar Topics I Specialize In
From enterprise AI strategy to RAG architecture, agentic AI to AI governance — the eight thematic pillars where I deliver end-to-end expertise. Each pillar page is a topical knowledge hub with the related blog posts, training, case studies and learning content.
Pillar pages: holistic topic resource maps
The pillar architecture treats a primary topic (RAG, agentic AI, LLMOps, AI governance, prompt engineering, enterprise AI, AI use cases, enterprise AI training) as a holistic resource page — bridging subtopics and serving as a clustering anchor for AEO/SEO.
Each pillar is a 3–5k word in-depth editorial covering definition (term + Wikidata entity grounding), use cases, architectural decisions, common pitfalls, vendor comparisons, ROI/cost signals, and a production checklist — so the reader has a single-page 'what I need to know on this topic' resource.
Pillar pages also establish a hub-spoke bridge across blog posts, glossary entries, use cases, learn modules, and consulting landings: subpages link up to the pillar, and the pillar links down to specific subpages. This shape supports both discovery and Google's topical authority signal.
From an AEO perspective, pillar pages match the structure preferred by AI search engines (ChatGPT, Claude, Perplexity): TLDR + definition + numbered claims + FAQ + speakable markup. When a user asks 'what is enterprise RAG', the pillar's opening paragraph is in a directly citable answer format.
- Each pillar is 3–5k words, comprehensive, and relatively stable (2–4 updates per year).
- Hub-spoke: pillar → blog/glossary/use-case/learn subpages.
- AEO-optimized: TLDR, FAQ, schema.org markup, Wikidata entity grounding.
- Pillar list is growing; subscribe to RSS for new pillar topics.
Enterprise AI Consulting
Enterprise AI consulting is the end-to-end discipline that takes AI from business objectives to technical architecture, prioritizing use-cases and shaping a production-ready roadmap so AI scales sustainably inside the organization.
View pillar →RAG (Retrieval-Augmented Generation) Architecture
RAG (Retrieval-Augmented Generation) is an architecture that grounds large-language-model answers in chunks retrieved from the organization's own documents or data sources, providing both freshness and citations.
View pillar →Agentic AI and Autonomous Systems
Agentic AI is the architecture in which a large language model — instead of producing a single answer — autonomously completes multi-step tasks by combining planning, tool use, memory and feedback loops.
View pillar →LLMOps: Production-Grade LLM Operations
LLMOps is the engineering discipline that covers the development, deployment, monitoring, evaluation and cost management of LLM-powered applications — extending classic MLOps with prompt versioning, eval-driven CI and observability tailored for non-deterministic systems.
View pillar →AI Governance and EU AI Act Compliance
AI Governance is the corporate framework that ensures AI systems — from design to use — meet ethical, safety, transparency, explainability and legal-compliance requirements (EU AI Act, GDPR/KVKK, ISO 42001).
View pillar →Corporate AI Training
Corporate AI training is a structured program — calibrated to different role levels from executives to engineers — that builds AI capability through hands-on, scenario-grounded learning with measurable outcomes.
View pillar →Industry AI Use Cases
AI use cases are a pragmatic decision guide — across banking, healthcare, retail, public sector and beyond — capturing the concrete business value, success metrics and reference architectures that make AI worth building.
View pillar →Prompt and Context Engineering
Prompt engineering is the applied discipline of designing instructions, examples, context and output controls so that an LLM produces consistent, accurate and cost-efficient outputs.
View pillar →Frequently Asked Questions
- A pillar is the center of the 'hub-and-spoke' SEO/content model: a single broad topic gets a deep 'pillar' page + 'cluster' pieces that cover specific facets. It concentrates topical authority and strengthens internal linking.