The travel advisors building the most consistent, high-touch client experience right now are not working harder than everyone else. They are doing the same tasks differently. Specifically, they have stopped re-answering the same thinking every time a new client emails, a new restaurant needs researching, or a DMC brief needs writing. They have built custom GPTs trained on their own standards, voice, and workflow and those GPTs do the repetition while they do the work only they can do.

There is a version of AI use that most travel advisors have already tried: opening a chat window, typing a request, getting something decent, tweaking it, and closing the tab. The next time a similar task comes up, they open a new chat and start over. That is not a system. That is using a very fast Google.

Heather Keller, founder and luxury travel advisor at Perfect Landing Travel in Houston, spent a decade building a referral-driven business before she started thinking seriously about how AI could actually move the needle. Not as a novelty, not as a shortcut, but as infrastructure. After ten years of developing consistent client experiences, she had a repeatable process worth replicating. What she needed was a way to give her team that process without being in every conversation herself.

Custom GPTs inside ChatGPT became that infrastructure.

Why Custom GPTs Are Not the Same as Using ChatGPT

The distinction matters more than most people realize.

Opening ChatGPT and making a request is, as Keller describes it, like walking up to the concierge at a luxury hotel. The concierge is smart and well-informed. They will give you good recommendations. But they do not know you. Every conversation starts fresh. The advice is built on general knowledge, not your specific standards, your preferred sources, or the way you communicate with clients.

A custom GPT is something else entirely. It is a trained team member. You define the task, the inputs, the voice, the sources to draw from, the sources to ignore, and the format of every output. You give it context about your business and examples of what good looks like. And unlike the concierge, it does not forget. Every team member who uses it gets the same trained version of your thinking.

Enterprise users who have invested in custom GPTs report saving 40 to 60 minutes per day on average on repetitive tasks. For a travel advisor managing proposals, client emails, flight quotes, restaurant research, and DMC communications, that time compounds quickly.

A custom GPT does not just know things. It knows how you work.

The shift in how you think about the tool is the first step. You are not asking AI a question. You are training a specialist.

How to Identify Which Tasks Deserve a Custom GPT

Not every task needs one. The candidates are the ones that show up the same way, over and over, with every client.

Keller’s framework for identifying GPT-worthy tasks is straightforward: look at what is creating repetition and decision fatigue at the team level, not just at the principal advisor level. The goal is to remove the thinking that does not need to happen fresh every time.

Her current custom GPTs include:

• Client email composer. Handles initial responses to new inquiries, qualifying questions, and destination suggestions based on client parameters. For a client emailing with basic trip details, the GPT generates a response that matches Keller’s tone, asks the right follow-up questions, and offers destination options with brief, specific descriptions. Time saved per use: fifteen to twenty minutes.
• Client workflow guide. Trained on the TIQUE TEAS framework plus Keller’s own trip life cycle breakdown. A team member can input where they are with a client and get the next step, a task checklist, and guidance on whether the trip is better served by a DMC, hybrid booking, or piece-by-piece planning.
• Restaurant curator. Trained to pull from Michelin guides, Eater, and local editorial sources. Explicitly excluded from: TripAdvisor, Yelp. Outputs formatted for client emails, including reservation difficulty. Every recommendation arrives in a consistent format with current sourcing.
• DMC email formatter. Fed a PDF transcript from a client consultation (recorded via Plaud), it generates the DMC brief email, extracts the initial itinerary outline, and flags client preferences and allergies for the CRM profile in TravelJoy.
• Flight email formatter. Takes a raw flight quote from an airline website or booking tool and converts it into a clean, readable, client-friendly format. No more copied-and-pasted jumble.
• Rep company and hotel GPT. Handles preferred partner communication in a consistent format.

The pattern across all of them is the same: a task that was previously done from scratch, every time, now has a trained starting point that reflects Keller’s exact standards.

How to Train a GPT That Actually Works

Building a custom GPT is only as good as the training behind it.

The most common failure point is treating a GPT like a tool you set up once and never revisit. Keller’s approach is closer to how you would onboard a new team member: clear expectations, examples of excellent work, defined boundaries, and regular feedback until the output is consistent.

The training has three components.

Voice.

The GPT needs to know how you communicate. That means feeding it examples of emails you are proud of, tone guidelines, language you want it to avoid, and the level of formality appropriate for your client base. A luxury advisor writing to high-net-worth clients has different voice parameters than an advisor primarily working with families on all-inclusive bookings.

Standards.

The GPT needs to know what good looks like in your business. For restaurant curation, good means Michelin-sourced, formatted for easy reading, with reservation difficulty noted. For a DMC brief, good means the right details pulled from a consultation transcript with nothing missed. Give it examples. Tell it what you expect every time.

Boundaries.

This is the step most people skip. The GPT needs to know where it cannot go. Which sources are off-limits. Which decisions it should flag rather than make. What it should do when it does not have enough information to proceed. Without boundaries, AI defaults to what it calculates as the generally acceptable answer, which may have nothing to do with your standards.

Once a GPT is built, Keller’s rule is simple: if an output is wrong more than three times in a row, she goes into the GPT and adjusts the training. Usually, it takes only a few minutes of tweaking. The standard she is working toward is a GPT that thinks the way she does on her best day.

The Difference Between Consistency and Automation

This distinction is worth naming directly, because it changes how you think about the goal.

Automation, in the travel business context, already exists. CRMs have automation. Email sequences have automation. Automating a task means removing a human from it entirely. That may be appropriate for reminders and confirmations. It is not appropriate for the communications and decisions that make a luxury client feel taken care of.

Consistency is different. Consistency means that whether Keller is handling a client email or a team member is, the output reflects the same standards, the same voice, the same thought process. The human is still in the loop. The thinking behind the output has simply been codified so it does not have to be re-created from scratch.

Keller’s clients are not looking for faster responses. They are looking for responses that feel considered and personal. The custom GPTs she has built make it possible to deliver that feeling at scale, across a team, without requiring her to be in every communication.

The operating framework that makes this work is: intake, output, polish, delivery. A client inquiry comes in. The GPT generates a strong draft. The advisor adds the personal layer. The client receives something that feels entirely individualized. The GPT handled the skeleton. The advisor provided the judgment and relationship.

That is not replacing the human touch. It is protecting it by removing the work that was depleting it.

A Note on Prompting Before You Build

Custom GPTs require setup and training time. But the skills that make a good GPT also make you better at using any AI tool, and they start with learning how to prompt correctly.

The most common mistake is treating AI like a search engine, putting in a topic and expecting a useful answer back. AI does its best work when you give it context, describe the outcome you want, and tell it to ask clarifying questions before proceeding. That last piece is a low-effort upgrade available to anyone using AI today: before asking for a deliverable, say explicitly, “Ask me any clarifying questions so you can give me the best possible result.”

The other foundational move is showing it what good looks like. Before asking an AI tool to write something for you, give it an example of writing you are proud of. Tell it that is the standard. The output will be noticeably better from the start, and your training time on a custom GPT will be shorter.

Keller is presenting a live demo of how she uses her custom GPTs inside the Niche community on July 2nd, where members can see the tools in action and ask questions in real time.

Where to Go Next

If you are ready to build a travel business that runs on documented, repeatable systems rather than starting from scratch every time, Niche is where that work happens. It is TIQUE’s membership community for serious travel advisors, with training, resources, and a room full of people building businesses the same way you are.

Join Niche and get access to Heather’s July 2nd custom GPT demo.