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.)
The trust insight is huge - I'm dealing with the exact same thing with FursBliss (dog health tracking). Users don't question if the AI is accurate, they question if they can trust it enough to make decisions.
Two things that worked for us: (1) showing the reasoning behind estimates, not just the number, and (2) letting users override the AI when they think it's wrong. Paradoxically, giving people an "edit" button makes them trust the automated output MORE because they feel in control.
Your real example trips idea is smart. It's like showing "here's what this looks like for someone else" so they can pattern match before committing.
That parallel is incredibly helpful — especially coming from a completely different domain.
It’s fascinating how the pattern repeats: users don’t attack the math, they question whether they can trust it enough to act on it.
I’m starting to think that early-stage AI products are less about intelligence and more about perceived control.
Out of curiosity — when you added overrides, did you notice more engagement or just less hesitation?
I think you are unto a real problem here, the pain is very valid for every budget traveller. As regards your question, I saw a recent post by @liutauras which might be of help.
Good luck!
I appreciate that — especially the “every budget traveller” framing.
That’s exactly the segment I’m trying to understand better: people who care about optimization, not luxury.
I’ll check out the @liutauras post — thanks for pointing me there.
Thanks so much for the shout-out! Anytime down to connect and talk this through. My X is linked in my profile :))
You are welcome sir. I have been reading some of your contents and I might reach out one of these days
<3
The trust insight is huge. I've learned similar lessons building AI-powered apps like Healthien (calorie tracking via photos). Users don't just want accurate results, they want to understand why the AI made those decisions.
Showing the reasoning behind estimates is brilliant. In my experience, people trust AI more when they can see the logic, even if they can't change it. The real example trips idea should help a lot with that initial leap of faith.
One thing that's worked well for us: letting users correct the AI when it's wrong. Paradoxically, giving people an "override" button makes them trust the automated output more because they feel in control.
It’s interesting how similar the trust dynamic is across travel and health.
“Even if they can’t change it” is a powerful point. It suggests that transparency alone may increase confidence, even before adding full override control.
I’m now thinking about structuring the UI so that users see:
– assumptions
– ranges
– and optionally edit values
Instead of just showing a final total.
When you introduced correction/override, did you see any downside (e.g. users breaking the logic), or was it purely positive?
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.
“Explainability matters more early on” — that line resonates.
I’m currently using single-point estimates, but I’m starting to think ranges might communicate realism better.
Instead of:
Total: $1,420
Maybe:
Expected range: $1,300–1,700 depending on season and accommodation choice.
Do you think ranges increase perceived realism, or do they risk adding ambiguity?
You’re not really selling a fixed price — you’re selling an expectation of what something might cost. In that sense, a range feels more honest.
If you present a single number and the real cost ends up higher, that’s the moment people remember. If it ends up lower, they’re pleasantly surprised. But that one negative surprise can outweigh several positive ones.
A range sets expectations more realistically. The challenge, of course, is that people naturally anchor to the lower end of the range — so that’s a tradeoff as well.
It’s not an easy balance, but I do think ranges communicate uncertainty in a healthier way.
Good luck
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.