Case Study: Making One-on-One Advice Scale
TeachingTravel LLC is a business with a working, pre-launch product that combines client profiles with current travel and credit-card information to produce personalized guidance. I designed the product concept, intake logic, content structure, and workflow — and used AI to automate the matching and drafting that would otherwise take substantial one-on-one effort.
STATUS: pre-launch, no clients yet • full newsletter tool and business website built and functional, end-to-end, with AI
The challenge
Personalized travel and credit-card content is genuinely useful, but only if it’s actually relevant — to someone’s specific cards, their travel interests, the deals that apply to them right now. Generic travel newsletters solve this by not solving it: they send the same content to everyone and hope some of it lands.
The key insight
Instead of choosing between “generic newsletter at scale” and “one-on-one advice that doesn’t scale,” the real opportunity was to make one-on-one relevance scale — using each client’s own data as the input, and letting AI handle the matching and drafting that would otherwise take hours per client per issue.
What I did
- Built an interactive tool that generates each client’s newsletter directly from their intake data, matching relevant card news, deals, and reminders to their specific card portfolio and interests
- Built a system that tracks card benefits and credits against their expiration windows, so the newsletter can remind clients to use what they’re already paying for before it lapses
- Built a full business website — homepage, real trip examples, benefits tiles, consultation booking, newsletter signup — end-to-end with AI development tools, rather than a traditional page-builder or agency
What’s next
Extending the tool so clients can update their own travel and card profiles and manage their own subscription preferences directly, rather than through manual intake — plus a series of AI-generated educational videos on points, miles, and credit card strategy.
What I’d do differently
If I were starting today, I’d bring AI in earlier — not to refine a solution, but to help define the problem itself. Looking back, most of my AI use on this project fell into three familiar categories: using it to sharpen the wording on an overview presentation explaining points and miles, using it to identify a system to build an intake form, and using it to tweak that form once built. What I skipped was using AI as a genuine thought partner to question the premise: should TeachingTravel even offer an overview presentation? Personalized consultations? I’d started from my own idea of the solution and used AI to execute it, rather than starting from the open problem and thinking it through together.
That’s a habit I’ve since changed — and it turned out not to be a new skill so much as a familiar one. I’ve spent my career bringing people to a shared understanding of a problem before deciding what to do about it; a whiteboard full of colleagues was usually how that happened. This time, AI is the one at the whiteboard instead.
That’s also why the next step isn’t another feature. Before building further, I plan to sit down with AI and genuinely reassess the whole business proposition from the ground up — what TeachingTravel should actually offer, to whom, and how — rather than continuing to build out the plan I started with.
Current stage
TeachingTravel is still in the workshopping phase — no paying clients yet, and no manual version of this newsletter was ever sent to compare against. The tool described above is built and functional; what’s still ahead is launching it to real clients.
What this demonstrates
- Designing, building, and launching a real business — newsletter tool and website — entirely with AI tools
- Applying AI to an actual operational bottleneck, not building something just to prove I could
- Using AI as a thought partner to help define the problem and the appropriate solution