We match you to the resort that actually fits — based on what you care about, not just price and star ratings.
Every resort looks the same in the photos. The reviews contradict each other. One says "amazing food," another says "buffet was terrible." You care about specific things — real cocktails, a good beach, no surprise fees, a resort that's not overrun with kids — but no search engine lets you filter for any of that. Price and star ratings tell you almost nothing about whether a resort will actually fit you.
Venn starts a layer earlier. Instead of asking you to sort through 50 hotels on an OTA, it asks what you actually care about — food style, drinks, vibe, amenities, budget, deal-breakers — and then finds the resort that genuinely fits those specific things. You don't have to become the researcher. You don't have to read 200 reviews to figure out which ones were written by people who want the same things as you.
Your quiz answers — food style, drinks, pool and beach preferences, resort size, deal-breakers, budget, kids' needs — get compiled into a private profile. We then run a triple-pass analysis using Anthropic's Claude language model. Three independent runs, each comparing your preferences against a candidate set of real resorts for your chosen destination and dates, then a fourth synthesis pass that reconciles the three and picks the strongest consensus match.
The triple-run design exists because language models can be inconsistent on a single pass. Running three independent analyses and then synthesizing them catches the cases where one run gets fixated on a single factor and misses a better overall fit.
After the match is chosen, we run a real-time price check against Google Hotels pricing data. If the real price exceeds your budget, we reject the recommendation and ask the matching engine for a cheaper alternative. We'd rather drop a beautiful resort than put you on the hook for a surprise bill.
The result page is built around a simple idea: validation with override. We give you one confident top pick with the full reasoning — and then we show you the alternatives we considered, with honest explanations of what's great about each one and why it ranked lower for your specific preferences.
We'd rather you see the reasoning and override it than feel trapped into a single choice. The override clicks teach the matching engine, too — if lots of people override us on a specific type of recommendation, we know our ranking logic needs tuning.
Venn also works for groups — friends, family, extended family reunions. When you start a group trip, two extra layers kick in before the resort match runs:
The coordinator suggests destinations when they start the trip. Those suggestions appear pre-checked in everyone's quiz, but anyone can untap one that doesn't work for them, and anyone can add new destinations via a dropdown. Once everyone has filled out their preferences, we find the intersection — the destinations that work for everyone. If no single destination works for everyone, we fall back to the most-voted option and flag the conflict so the group can decide whether to override.
The coordinator sets a broad window. Each person then picks which specific weeks within that window work for them. We find the intersection of available weeks. This is usually the layer that surfaces hidden conflicts — weddings, school events, work trips — that would've taken five rounds of group chat to find.
After destination and date convergence narrow the problem, the preference matching runs against everyone's quiz answers at once. The result shows per-person breakdowns explaining why the resort works for each individual — and each person's private budget, deal-breakers, and preferences are never visible to the rest of the group.
Solo example. One traveler, November 2026 trip to Mexico, budget $2,100 per adult for 7 nights. Prefers authentic Mexican cuisine and proper craft cocktails in real glassware, not frozen margaritas. Deal-breakers: watered-down drinks, boring buffet, overcrowded pools.
What the engine did. Triple-run analysis ruled out mega-resorts (crowded pools), ruled out properties known for weak drink programs, and surfaced Iberostar Grand Paraíso as the top pick — solid mezcaleria, authentic cochinita pibil at the Mexican restaurant, mid-size footprint, within budget. Ranked alternatives included Hotel Xcaret México (beautiful property, but over budget for this traveler), Secrets Maroma (great food program, but adults-only and less interesting for cultural exploration), and Grand Velas Riviera Maya (better food, but $550+/night blows the budget). Each alternative came with a booking link in case the user disagreed with the ranking.
What didn't happen. The user didn't read 40 reviews. Didn't compare 15 OTA tabs. Didn't wonder whether "amazing food" in a review meant the same thing they mean by amazing food. They tapped 7 questions, saw one confident pick with reasoning, and had the option to book any of the alternatives if they disagreed.
Being honest about this because it's a question affiliate reviewers and users both ask.
We use Anthropic's Claude model for:
We don't use AI for:
Your quiz answers are stored against a private code and are never shared with anyone else. If you're planning with a group, each person's budget, deal-breakers, and preferences stay private — only the matching engine sees them, never the coordinator or other group members. See the Privacy Policy for full details on what we collect and how it's handled.
Free. 30-second quiz. One confident pick plus ranked alternatives you can book.
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