What this is
AI is most useful when it has a specific job. Summarizing the data a team already reads. Drafting the content someone already writes. Flagging the records that would otherwise get missed. Recommending the next action from numbers someone is already looking at.
The question is not whether the model demos well in isolation. The question is whether the system helps a real person decide, fix, inspect, or publish with more confidence than they had yesterday.
Selected projects
Monday Morning Metrics
AI-assisted ecommerce analysis turned into one recommendation for the week ahead.
The AI layer reads structured store metrics and turns them into a short, practical suggestion: what changed, why it may matter, and what action is worth considering next. The output is one paragraph in an email, not a long report nobody reads.
Showcase A redacted weekly email, the metric inputs, and examples of recommendations tied directly to store data.
chronoMelon Product Center
AI-assisted catalog audits and grounded content work inside a product operations platform.
Product Center uses AI around a canonical product model: flag weak or inconsistent catalog fields, draft copy from structured data, and answer product questions without treating the sales channel as the source of truth.
Showcase Catalog audits, grounded copy generation, product Q&A, and visible sync context from WooCommerce and future channel adapters.
How I approach it
My bias is toward visible inputs, bounded outputs, and a human decision nearby. The system should be inspectable, the output should be reviewable, and the person on the other end should be able to tell when it’s right and when it isn’t.