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Data tells you what happened. AI tells you what to do next.
I help businesses turn raw data — reviews, records, spreadsheets, logs — into clear, usable insight, using AI to do the reading so you can spend your time on the deciding.
What AI data analysis actually means
Most businesses already have the data they need to make better decisions — it's just scattered across spreadsheets, review sites, CRMs, and files nobody has time to read cover to cover. AI data analysis is the practice of pointing a model at that mess and asking it to find the pattern, not just store the record.
Done well, it's not a black box that spits out a number. It's a process: clean the data, ask it the right questions, and translate the answer into something a business owner can act on the same day — not a report that sits in an inbox.
That's the work I do — building small, focused tools that take a specific kind of data (customer reviews, operational logs, financial records) and turn it into a decision-ready summary, automatically, on a schedule you control.
Where this applies
Customer feedback analysis
Turning scattered reviews into sentiment summaries, recurring complaints, and ready-to-send responses.
Reporting automation
Building dashboards and recurring reports that pull from live data instead of manual copy-paste.
Data pipeline & database work
Structuring and maintaining the underlying systems that make fast, reliable analysis possible in the first place.
Background before the AI work. Nearly three decades as a data engineer and database administrator, most recently managing environments with over 4,000 databases as a Data Engineer III.
That's the part that doesn't show up in a flashy demo: knowing how data actually behaves at scale, where it breaks, and how to build something that keeps working after the first week. AI is the new layer on top — but it's only as good as the data foundation underneath it.
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Have a pile of data you haven't had time to read?
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