AI platforms are becoming another place where customers discover, compare, and evaluate businesses. Most brands still have limited visibility into how they are represented, which competitors appear, or what sources are shaping those answers.

I work across AI visibility measurement, prompt and competitor research, citation analysis, structured data, content architecture, and the signals that help AI systems better understand a business.

More data does not automatically lead to better decisions. Teams often have reporting spread across platforms, inconsistent definitions, manual processes, and dashboards that explain what happened without helping determine what to do next.

I have built reporting and analytics systems that bring together business, marketing, commerce, and customer data, with a focus on making the output useful for the people actually making decisions.

Many useful AI ideas stall because they remain disconnected from the systems a business already uses. The opportunity is often less about adding another AI tool and more about connecting existing processes in a smarter way.

I build lightweight automations, integrations, internal tools, and AI-enabled workflows using APIs, webhooks, MCP, and custom applications. The goal is to remove repetitive work and make useful information easier to access and act on.

Commerce teams have enormous amounts of customer, product, media, and transaction data. The challenge is connecting those signals to understand what drives acquisition, repeat purchases, customer value, and growth.

My work has included customer journey analysis, product affinity, acquisition and retention behavior, forecasting, retail media measurement, attribution, and identifying patterns that can inform what a business does next.