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Forrest Miller

Product strategy

TripSapien

Personalized trip plans you can trust — curated recommendations, checked for your dates.

Open tripsapien.com (opens in a new tab)

Who it's for
Type-A travelers who over-research a trip, then lose a morning to a locked door.
The problem
Travelers collect recommendations for weeks — friends, blogs, Reddit, ChatGPT — then re-check every place by hand against their dates.
What it does
Pick a city and dates; pasting research is optional. Places come from a 420,000-venue catalog, checked against your exact dates and grouped into neighborhood days.
TripSapien's mobile homepage: 'Personalized trip plans you can trust — zero AI hallucinations', with curated recommendations, date checks and neighborhood day plans called out above the city search.
tripsapien.com — plans you can trust
A London trip's Places tab on mobile: Locanda Locatelli flagged 'Permanently closed' at the top, above real place rows with photos, Yours and Recommended ribbons, and Book actions.
The trip — every place checked for the dates
The Itinerary tab on mobile: Day 1 'Art Day' with morning, dinner and evening slots, visit lengths, open hours, and walking and ride times between stops.
Itinerary — scheduled days with travel times
The Areas tab on mobile: the South Bank neighborhood with its editorial blurb and the trip's places grouped under it.
Areas — the trip grouped into neighborhood days

Opportunity

Trip planning eats 18 hours per trip — and it is moving into AI.

~25 million US adults formally plan trips, and each one costs about 18 hours of planning and booking. The work is moving into AI: 37% of US travelers now plan with it.

The bet: paste your messy research — friends' texts, blog lists, a ChatGPT draft — and AI turns it into one organized trip that grows a library of real itineraries.

What I learned

The organizer worked. ChatGPT kept the planners.

The organizer shipped and worked. Travelers used it — then went back to ChatGPT to keep planning. Organizing a paste was a convenience, never a reason to switch.

The durable problem was the one AI planning had just created: 90% of AI itineraries contain at least one inaccuracy, and 1 in 4 recommends a closed venue. Verification became the work. The product to build was the checker.

Pivot & results

Become what AI planning made necessary: the checker.

What changed

  1. Became the checker. The homepage stakes the claim — “Zero AI hallucinations” — and every place resolves against a vetted catalog of ~420k places, checked against the trip's actual dates.

  2. Kept the recommendation flywheel. The catalog grows by thousands of places a day; the paste is now optional.

  3. Rebuilt acquisition on free public data. Long-tail city-month pages carry the search traffic; free Foursquare and OpenStreetMap data replaced the paid-lookup cost wall.

Measured results

6,936

Google clicks on city-month pages

772

visitors referred by AI assistants, most from ChatGPT

Corrections & failed tests

52%

of raw search visitors were automated crawlers — removed from conversion reporting

22 cut → 20 restored

cities cut as duplicates by one test, restored by three — the wrong audit stays on file

What's next

Neighborhood pages shipped in late July; the ranking verdict comes when the indexing window closes.