Business AI consultant & engineer
Business AI / automation / technical systems
I design practical AI workflows, automation systems, and technical resources for teams that need clearer execution, faster processes, and better business systems.
From strategy to implementation, I help turn AI ideas into working workflows, automations, and clear technical resources.
Identify where AI belongs in real operations, design the workflow, and define a practical implementation path.
Build repeatable systems for reporting, documentation, handoffs, content operations, and operational support.
Map workflows, identify bottlenecks, translate business needs into technical requirements, and make execution clearer.
Create clear documentation, implementation guides, and technical content for AI, automation, and business systems.
Practical guides and analysis covering relevant issues in the AI space geared towards business needs.
AI coding agents do not just need bigger context windows or better prompt files. They need a controlled memory layer that survives across sessions, tools, and vendors. This article explains why hooks may matter more than MCP alone, how durable agent memory should work, and why memory ownership is becoming a serious business architecture decision.
LLM scaling is not just a brute-force story. MIT research on superposition suggests bigger AI models may improve because they give overlapping internal representations more room to interfere less. That helps explain why scale still matters, but it also shows why businesses need model selection, evaluation, workflow design, and cost discipline.
Lesson Identifying AI use cases in business environments. Learning Objectives Prerequisites No coding is required to understand this lesson. Helpful background: AI use cases…
I help turn messy processes, AI ideas, and technical ambiguity into practical systems teams can understand and use.