vincentalbouy

Yes you are right, we have a lack of supply there, and we do not show places that we are not able to Book. Chat GPT is showing data, we are showing bookable venue. But I agree that in Peru our supply is too limited so the experience is not ideal. Thank you for reporting, I appreciate the time spent

Grammar mistakes is a solved problem yes.

Thank you , Interesting but not related

Hi Anjel, thank you for testing. What do you mean?

Thanks a lot for this insight. Genuinely appreciate you taking the time to help me.

I 100% agree, the interface is the least interesting part. Anyone can build a chat UI. That’s not the moat.

What matters is the messy stuff in the backend. Vendors. Hotels. Quotes that change. Someone forgetting to update availability. Contracts. Deposits. Random edge cases. That coordination layer is the real product.

The UI is just the tip. The hard part is keeping state across dozens of moving pieces and async back-and-forth.

My belief is that AI finally makes some of this operational glue automatable. Not in a magical way. But in a very practical loop:

ask → get info → update plan → trigger action → wait → adjust → repeat

Planning is basically structured ping-pong. It’s not search. It’s evolving constraints over time. That’s why it feels agent-shaped.

What I am basically saying is : Event planning is really AI agent prone and very conversation prone, that is why this kind of interface will take over travel and event planning. It is like you have a personal travel agent sitting next to you and it showing you options.

Totally agree with you though. organizing fragmented supply is the hard, unsexy, painful work. That’s where the real value is built.

Thank you for reporting that problem. I appreciate the help. The agent is supposed to checks real flight API so this is weird, let me investigate that further.

This is interesting thank you for reporting that. I am goi t o investigate that use case deeper. The models at core are Gemini / ChatGPT / claude so you should get a similar level answer or better (because of our data) not worse. I think in the VISA, we may have to do a specific training and setup of the system prompt.

Just so I know, you asked the exact same thing in both ChatGPT and our product? no changes?

Maybe the algorithm was too pushy on venue selection and not enough focused on the understanding of your problem.

Actually I have been of both side of this story.

2019 -- 2020 : we had an office in New York with my previous company for 30 people

2020 -- 2026 : We are fully Remote at TeamOut, we are 30 and we only do retreats

We pend 5X less than a company going to an office everyday

Thank you Daniel, it makes sense to me

Agreed. Thank you for the support. There was even grammar issues in my response, what sort of Ai is doing grammar mistakes?

I am asking because I want to see if my Cambridge (MA) supply overall is too expensive. Have you tried asking for cheaper options? Thank you

Thank you for trying it and giving me a feedback.

Cool that it adapted from UK to Massachusetts. Do you remember what was the price per person per night? Our client usually book around $200-500 room rate per night.

Yes we manage the whole booking process, I think the true value is here as aggregating venues with AI nowadays is quite easy .

For questions between venues and client, we have a specific chat but the Agent can also try to reply if it knows the answer or decide to contact the venue via email (we haven't released that tool yet)

Thanks

Thank you for reporting that. It is a real issue.

We are actually pushing an update on that, we want to make the distinctions between Exact Matches (respecting destination criteria) and Non Exaft Matches , that. are suggestions.

The below options were suggestions (because we have limited supply in Netherlands ) but it not clear at all for the user:

2. Marysville, Ohio, United States 3. Lisboa, Portugal 4. Nashville, Tennessee, United States 5. Kenmore, Washington, United States 6. Golden, Colorado, United States

Thank you for your help

Hello, the visa question is quite recurring especially for larger team that have distributed teams.

The algorithm is quite smart about that because it has legacy data from ~1500 events. IT has been shown a lot of example of" When a team is located there, they have VISA issues --> they should actually go there because it worked in the past for XYZ client"

For example the algorithm will know that Dominican Republic is usually VISA frienly and can send larger groups there etc.

For the second part: Finding a venue with the requirements is hard, our clients really have a pain point, but what it is even harder tis to agree on terms, prices, food, meeting space, transportation coordination etc, it is a logistical nigthhmare