Validating an AI travel budgeting tool — early feedback surprised me
A few months ago I built a small AI tool to solve a personal problem.
I was planning a trip and realized the hardest part wasn’t choosing the destination.
It was structuring the total cost realistically.
Flights were everywhere.
Hotels varied massively by area.
Transport decisions changed the budget completely.
So I built a small decision engine that estimates total trip cost and breaks it down by category.
No booking.
No inspiration.
Just clarity.
I recently got my first real feedback from a user:
“Budgeting was the most stressful part. Showing real example trips would increase trust.”
That changed how I see the product.
The problem might not be estimation accuracy.
It might be trust.
So now I’m adding:
– Real example trips
– Downloadable structured PDFs
– Clear breakdown visuals
For those building SaaS:
When validating early-stage products,
how do you increase trust in AI-generated estimates?
Would love feedback from founders here.
(If anyone wants to test it, happy to share privately.)
In my experience, trust increases when users can see the logic behind the numbers.
Clear assumptions, ranges instead of single numbers, and real example cases go a long way.
Accuracy matters — but explainability matters more early on.
Sounds like you’re moving in the right direction. Good luck — would be interesting to see how it evolves.
The trust insight is really sharp. I build finance tools for small businesses and ran into the exact same thing — users didn't question whether the categorization was wrong, they questioned whether they could trust it enough to hand to their CPA.
Two things that moved the needle for us: (1) showing the reasoning behind estimates, not just the number, and (2) letting users override/correct the output easily. The moment people feel like they can adjust your AI's answer, they paradoxically trust it more — because they feel in control.
The real example trips idea is smart. In finance tools the equivalent is "here's a sample categorized bank statement" so users can see the output format before committing. Reduces the leap of faith significantly.
This is incredibly helpful — thank you.
The “reasoning visibility” point really resonates. Right now I focus heavily on generating structured totals, but I haven’t fully exposed the assumptions behind each estimate.
I can see how showing:
– price ranges used
– travel style assumptions
– seasonal adjustments
could dramatically increase trust.
And your point about overrides is powerful. It makes sense that control increases trust rather than reducing it.
Out of curiosity — when you introduced overrides in your finance tool, did it increase engagement immediately or did it mainly reduce churn?
Really appreciate the parallel to CPA handoff. That framing helps a lot.