Insights
Insights on AI, Data, and Salesforce Architecture
Practical notes, implementation lessons, and architecture thinking around AI, LLM/RAG, data engineering, Salesforce Marketing Cloud, Data Cloud, CRM/CDP activation, and marketing automation.
Featured articles
Start with the articles most connected to practical implementation.
Building AI Assistants for Internal Teams: Start with the Workflow
Internal AI assistants work best when they are designed around real tasks like support triage, documentation search, marketing operations troubleshooting, QA, and reporting help.
How Salesforce Data Can Power Better AI Workflows
Salesforce CRM, Marketing Cloud, Data Cloud, preferences, campaign history, and customer profile data can support stronger AI workflows when structured and governed properly.
RAG Is Not Just a Chatbot: It Is a Knowledge Architecture Problem
Retrieval-augmented generation works best when teams treat documents, metadata, permissions, retrieval relevance, and answer evaluation as core architecture decisions.
All insights
Articles grouped around practical implementation themes.
Building AI Assistants for Internal Teams: Start with the Workflow
Internal AI assistants work best when they are designed around real tasks like support triage, documentation search, marketing operations troubleshooting, QA, and reporting help.
When to Use Agentforce, OpenAI, Anthropic, or Google AI
A practical framework for choosing AI tools based on workflow fit, data access, governance, integration needs, and user experience.
The Hidden Data Problems Behind Marketing Automation
A practical look at duplicate profiles, unclear sources of truth, fragile journeys, outdated segments, missing consent logic, and weak reporting data.
How Salesforce Data Can Power Better AI Workflows
Salesforce CRM, Marketing Cloud, Data Cloud, preferences, campaign history, and customer profile data can support stronger AI workflows when structured and governed properly.
RAG Is Not Just a Chatbot: It Is a Knowledge Architecture Problem
Retrieval-augmented generation works best when teams treat documents, metadata, permissions, retrieval relevance, and answer evaluation as core architecture decisions.
Why AI Projects Need Data Architecture Before Model Selection
A practical look at why successful AI implementation depends on trusted data, workflow context, and strong integration patterns.
Contact
Want to discuss AI, data, or Salesforce architecture?
I’m open to consulting, technical advisory, architecture reviews, and relevant professional opportunities.