How to Choose an AI Trip Planner for Your DMO
Four approaches. Very different tradeoffs. Here's what to look for.
Visitors don't want to browse a list of things to do. They want a trip that feels like it was built for them — their dates, their group, their interests. The question for DMOs is how to deliver that at scale.
There are four ways to get there. Each involves real tradeoffs.
How the Approaches Compare
| General-Purpose AI | Agency Custom Build | Enterprise Platform | White-Label (Getaway) | |
|---|---|---|---|---|
| Time to live | Already live (not on your site) | 4-8 months | 2-4 months | Under 2 weeks |
| Cost | Free (but you get nothing) | $50K-$150K+ build, plus ongoing | $2K-$10K/month, annual contracts | Flat monthly fee, no per-user pricing |
| Local accuracy | Low. Recommends closed venues, misses seasonal context | High, if maintained | Moderate. Generic data across thousands of destinations | High. Trained on your destination specifically |
| Your brand | None. Visitors see Google/ChatGPT | Full control | Limited. Platform branding visible | 100% your brand. Built around your destination's identity |
| Visitor data ownership | Zero. Data belongs to Google/OpenAI | Yes, if built that way | Shared or platform-owned | 100% yours. We never sell or share it |
| IT/dev staff required | No | Yes, ongoing | Some | None |
What to Ask Any Vendor
If you're evaluating AI trip planning tools for your DMO, these are the questions that separate serious solutions from demos that look good in a pitch meeting:
Can you show me a demo built for a real destination? If the vendor can only show a generic demo or a mockup, the tool probably isn't trained on local knowledge. Ask to see a deployment for a city comparable to yours.
Who owns the visitor data? If the answer isn't "you do, fully, with no exceptions," walk away. Your visitors' planning data is a strategic asset. It shouldn't be shared with the vendor's other clients or used to train their models for competing destinations.
What does my team need to do after launch? If the answer involves maintaining code, managing an API, or updating a content database, the tool wasn't built for a DMO marketing team. The right answer is: nothing. We handle it.
How do you handle accuracy? Ask about guardrails. Does the AI invent restaurant menus? Does it recommend businesses that closed six months ago? A serious tool has systems to prevent hallucination, not just a disclaimer that says "AI may make mistakes."
What happens if we cancel? No long-term contracts. No data lock-in. No "we own the content we generated." Your data should leave with you.
Why DMOs Are Choosing the White-Label Approach
The pattern we see is straightforward. DMOs that try general-purpose AI get frustrated by inaccuracy and lack of data. DMOs that pursue agency builds get frustrated by timelines and costs. DMOs that evaluate enterprise platforms get frustrated by the one-size-fits-all approach and lack of DMO-specific features.
The white-label approach works because it was designed for exactly one customer: destination marketing organizations. Not hotels. Not tour operators. Not OTAs. DMOs.
That focus means every feature decision is made through the lens of what a DMO marketing team actually needs: first-party visitor data, partner business visibility, personalized visitor experiences, grant-ready reporting, and zero technical burden on a team that's already stretched thin.
See it for yourself.
Our live Branson, MO deployment is fully functional. Plan a trip, see the experience, and decide if this is the right approach for your destination.
Try the Live Demo →