An AI that learns on its own from your team
Your front desk rates any reply from 1 to 5 and writes the correction; WhatsMinder turns it into a rule and gets more precise the more they use it, with no coding and no reconfiguring.
Every correction makes it better
Most AI agents stay exactly as they arrived on day one. WhatsMinder does not: your front desk team rates any reply from 1 to 5, writes what it should have said, and that correction becomes a rule the agent follows from then on. Every new question from a real guest becomes knowledge that stays in your hotel.
There is no need to open code or call support. The people who know your hotel, the ones who serve the guest every day, are the ones teaching the agent in their own words. The more your team uses it, the more finely tuned it responds to exactly how your property operates.
How WhatsMinder learns from your team
A simple loop that turns your front desk experience into an agent that gets sharper every day.
Your front desk grades every reply
Anyone who handles the hotel WhatsApp can score a reply from the agent, from 1 to 5. No technical profile and no training: whoever knows how to serve guests knows how to teach the agent.
- A quick rating on any reply
- Marks what was right and what was missing
- From the normal workflow, no separate screens
What your team writes becomes a rule
When a reply was not the right one, your front desk writes what it should have said, in the language of your hotel. WhatsMinder turns that correction into a rule automatically and applies it in the next conversations.
- The correction is written in your own words
- It becomes a rule without any coding
- The agent follows it from then on
Hands off to a human and logs the gap
When a question goes beyond it, the agent does not improvise: it passes the conversation to your front desk. The answer your team gives is recorded as learning for the next time someone asks the same thing.
- It never invents information about your hotel
- The handoff arrives with full context
- Every gap closed is one less question to escalate
Learns your operation, not generalities
It does not learn industry theory: it learns that at your property breakfast ends at eleven and that cabin four does not take pets. Your policies, your tone and your exceptions, exactly as your team lives them.
- Rules specific to your property
- Your tone and your style of service
- The exceptions no generic script covers
Progress is observed, not promised
On the dashboard you see how many conversations the agent resolves on its own and how many escalate to your team. Week after week you notice it resolves more and asks less, with the evidence in plain sight.
- Conversations resolved versus escalated
- The evolution visible on the dashboard
- Decisions based on evidence, not impressions
No option trees to feed
A menu chatbot dies because nobody updates its script. WhatsMinder does not depend on anyone feeding it: it matures with daily use and keeps what it learned even when staff changes.
- Nothing to reprogram or reconfigure
- Knowledge survives shift rotation
- Every day it replies better than the day before
A menu chatbot replies the same on day one hundred as on day one. WhatsMinder turns every correction from your front desk into a rule, so the agent of today always knows more than the agent of yesterday.
WhatsMinder versus the alternatives
What happens to your hotel’s knowledge depending on who handles the chat.
| WhatsMinder | Manual WhatsApp | Menu chatbot | |
|---|---|---|---|
| Replies in seconds, 24/7 | |||
| Improves with every correction | Depends on person | ||
| Adjusts without coding | |||
| Knowledge stays in writing | Only the script | ||
| Survives staff turnover | |||
| Detects gaps and logs them | |||
| Understands off-script questions |
The learning loop, in three steps
Your front desk masters it from day one, with no technical training.
- RateYour team scores any reply from the agent, from 1 to 5, right from the conversation.
- CorrectThey write what the agent should have answered, in your hotel’s own words.
- The agent adopts itThe correction becomes a rule and applies in the next conversations, with nothing to configure.