About Ally
Learning what makes something worth choosing.
I spent twelve years selling products and experiences whose value could not be reduced to a list of features.
I sold Italian wine to sommeliers in San Francisco, represented a Napa Valley estate winery and worked with Nespresso at James Beard dinners and Michelin events. For eight months, I joined the wine team at a Michelin-star restaurant.
The setting changed, but the central question did not: What makes this worth choosing, and what does this person need to understand before they can choose it?
In the wine business, the significance of a bottle was never just the producer or vintage. It was the story behind the wine, the reason it belonged with that particular dish and what the guests wanted from the evening.
The job was to notice what made something distinctive, then make those details useful at the moment of choice.
I did not know then that I was preparing for a problem involving artificial intelligence.
The second education
Learning how businesses make decisions.
I spent the next five years at NetSuite working with growing consumer brands and selling the enterprise systems behind their operations.
The questions were different. I had to understand what a company was trying to accomplish, what stood in the way and why solving the problem warranted the investment. Earning President's Club mattered, but the more lasting education came from seeing how leadership teams evaluate risk, establish priorities and decide when to act.
That experience taught me to move between the product story and the business case.
A recommendation can be strategically sound and still fail if it is disconnected from the company's commercial priorities, internal resources or stage of growth. Good analysis has to account for the business that will be responsible for acting on it.
Why I started this practice
A familiar problem in an unfamiliar setting.
When I began studying how AI platforms recommend restaurants and consumer products, I recognized something I had spent my career working on.
ChatGPT, Claude, Gemini and Perplexity were being asked to recommend food, wine and hospitality experiences they could never have themselves. They could not taste the product, visit the restaurant or understand intuitively why one choice suited a particular occasion better than another.
They had to reconstruct that understanding from the digital record surrounding the business. That record was often incomplete.
A restaurant could be widely celebrated while providing little evidence about its private dining experience. A winery could be known for one part of its portfolio while the varieties that distinguished it rarely appeared in recommendations. A product brand could earn substantial press without establishing when, how or why someone should choose it.
I built this practice to investigate that difference. I study which brands AI platforms recommend, the buyer situations in which they appear and the evidence supporting those decisions. I then identify where a client's actual strengths have failed to carry into the pages and independent sources AI can use.
The real business and the version available to AI were not always the same.
What my background changes
The judgment begins after the results arrive.
My research tools can run repeated prompt sets, organize hundreds of recommendations and reveal differences across platforms. That is the mechanical part.
A mention is not automatically valuable. A restaurant appearing in a broad "best restaurants" answer may care far more about whether it surfaces for an important client dinner. A beverage brand does not need to dominate every category conversation if its commercial strength lies in a specific occasion or use.
The research has to be evaluated against what the business sells, which customers it wants and what kind of growth matters.
My background in food, wine and hospitality helps me recognize when generic language has stripped away the details that make a product or experience worth choosing. My years in enterprise sales keep the recommendations tied to commercial priorities rather than visibility for its own sake.
I work with established food, beverage and hospitality businesses whose real strengths are more specific than the digital record surrounding them.
The work begins by establishing what AI platforms understand now, where that understanding falls short and which changes deserve attention first.
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