We built an AI-powered travel interface from scratch. Not a chatbot. Not a widget. Not a GPT-wrapper with a smiley face bolted onto a website. A full conversational interface with its own vector database, its own retrieval pipeline, its own memory system, and real-time streaming over WebSockets. Not to replace anything. To test whether conversational AI is actually ready to be a meaningful channel for travel. Here’s what we found, what it took, and why we think most of what’s out there right now is noise.

This is a two-part story. This first part covers the why: what problem we’re solving, what we learned from failing at this before, and why a plugin will never get you here. Part 2 goes deeper on the architecture: the RAG pipeline, the memory system, the UI/UX decisions, and the technical tradeoffs we made.

What are you actually trying to solve?

That’s the question I keep coming back to. Every time I see a company announce their new “AI-powered” feature. Every time someone asks me what we’re doing with AI. Every time a vendor pitches yet another plugin.

It’s the most important question in tech right now. And almost nobody is asking it.

Instead, the entire industry is working backwards: starting with “we need to do something with AI” and then looking for a place to put it. Companies hear “AI” and they hear “innovation” and they think they’re falling behind if they don’t do something right now.

The pressure to “do something with AI” has become louder than the question of what are you trying to achieve.

And so what happens? Companies reach for the most visible, most marketable application they can find: a chatbot. Slap it on the website. Give it a name and a friendly face. Connect it to a knowledge base. Done. “We do AI now.”

Here’s the problem: the integration is always limited. Some connect to a product database. Some pull live availability. But as a plugin, there’s a ceiling to how deep that integration goes. It’s always working with whatever data it gets access to, in whatever format the API provides, with whatever context the vendor decided was enough. The result is an interaction that looks conversational on the surface but is fundamentally constrained in what it can actually do.

And because it’s a plugin, it can never be fully seamless. It will always be a separate layer on top of your website, not part of it. A different design language. A different interaction model. A different experience. The customer feels it, even if they can’t articulate it. It’s the difference between a room that was designed as a whole and a room where someone placed a kiosk in the corner.

A smarter gatekeeper is still a gatekeeper. Your customers wanted a door.

We’ve been down this road ourselves. Back in 2019, we built the first version of Joy on Flow.ai. An intent-based chatbot that could understand natural language, extract travel preferences from a single sentence, and generate leads for our specialists. It worked. Technically. But customer engagement was too low to justify keeping it alive. We retired Joy in early 2022.

The lesson wasn’t that the technology was bad. The lesson was that people have learned to distrust chatbots. And rightfully so. For years, companies have used chatbots as a barrier to contact, even when they claim otherwise. Customers know this. They’ve experienced it. After three wrong answers, you don’t think “let me try again.” You think “how do I get to a real person?” Some people don’t even bother engaging. They see a chat bubble with a face and their immediate reaction is to close it. Not because they’re impatient, but because experience taught them it’s a waste of time.

That’s not a technology problem. That’s a trust problem. And it’s one you can’t solve by making the same format smarter.

On top of that, the interface of a chatbot plugin is simply limiting. A small window in the corner of your screen. A few lines of text. Maybe a button or two. You can’t show rich product cards in that. You can’t display a map of a trip. You can’t let someone browse, compare, save trips for later. The format dictates the experience, and the format is a box.

That experience is exactly why we didn’t build another chatbot. Not a smarter one. Not a better one. Something fundamentally different. A full conversational interface, built from scratch, designed to inspire. Where someone can explore destinations, discover trips they didn’t know existed, and when they’re ready, transition into contact with a specialist after a conversation that actually felt good. Not after three failed attempts to get past a gatekeeper.

The real question was never “how do we add AI to our website?” The real question is: where does AI genuinely create value that wasn’t possible before? That’s what we set out to test.

Why I built what I built

I didn’t build this because I think websites are dead or because I wanted to say we “do something with AI.” I’ve written before about how much I despise that mentality.

I built this because of a question I’ve been testing for over a year now: is conversational AI truly ready to be a real channel?

In my Web 4.0 post, I wrote about the autonomous web, a world where AI assistants don’t just retrieve information but act as independent digital consumers on behalf of their humans. Where Lisa tells her AI assistant “find me a two-week trip to Thailand” and the AI contacts travel companies through MCP-enabled interfaces, negotiates, refines, and presents options. Without Lisa ever opening a browser.

That might sound like removing humans from the equation. It’s not. The technology enables full autonomy. But that doesn’t mean we want to remove the human. It means the customer gets to choose. And our default will always be: lead to a person. More on that later.

That future is coming. Maybe not tomorrow, but it’s coming. And if you’re a travel company that takes itself seriously, you’d better be ready for it.

So I built Joy, an AI travel assistant for 333travel, not as a product launch, not as a marketing play, but as a serious test. Can we build a conversational interface that actually adds value? That understands our products? That doesn’t hallucinate as much? That creates a genuinely useful experience as an additional channel next to our website, phone, and email?

I didn’t build this to replace our website. I built this to answer a question: can a conversation be a meaningful channel for personalized travel?

What this actually is

Let me be specific about what Joy does:

Joy sits on top of our entire product database. Every roundtrip, every hotel, every excursion, every destination, all embedded as vectors in a dedicated database. When a customer says “I want something adventurous in Southeast Asia, around two weeks, not too touristy,” Joy doesn’t keyword-match against a PDF. She searches semantically across hundreds of products, reranks results based on actual relevance, and responds with options that genuinely fit the intent.

And she remembers the conversation. Not just the last message, the whole thread. She knows what trips were discussed, what was saved, what was dismissed. When you say “that trip you mentioned earlier with the cooking class,” she understands what you mean even if she originally called it a “culinary experience.”

But here’s the key difference with any chatbot out there: she doesn’t just know travel in general. She knows our travel. Our specific products. Our specific itineraries. Our destinations that our team personally visited. That’s not something you can easily bolt on with a plugin, no matter how smart the LLM behind it is.

And the moment you want human contact? Joy hands the full context to a real travel specialist who knows exactly what you’ve been exploring. No “please describe your inquiry again.” No cold transfer. Full continuity.

Joy doesn’t replace conversations with specialists. She makes them better.

What she doesn’t do (yet): she doesn’t handle customer service queries, airline information, or post-booking support. This is purely a product discovery and inspiration channel. We’ll get there. But we’re not going to pretend she does things she doesn’t. And honestly, when someone reaches out to customer service, there’s already a problem. That’s the worst possible moment to put a machine between you and your customer. That’s when you show up. Personally. So we are still debating about what we want her to do there. The nuance is always in the details.

Why you can’t get here with a plugin

You cannot achieve what we built by installing a plugin. Not with ChatGPT. Not with any off-the-shelf “AI widget.” Not with any SaaS tool that promises to “make your website smarter in minutes.”

And it’s not because the AI models aren’t good enough. The models are fine. The problem is everything around them.

The entire system depends on data that we own, structure, and control. Every trip in our database has been visited by our own team. Every itinerary is our own creation. Every piece of destination knowledge lives in our own systems. This isn’t data we license from a third party or pull from a brochure catalog.

That means we can embed it. We can structure it exactly how we need it for semantic search. We can update it the moment something changes. We control the full pipeline from raw product data to vector embedding to retrieval to response.

You can’t build a smart interface on top of piles of dumb data.

If your product data lives in someone else’s system, if you’re reselling trips from a catalog, if your “database” is a collection of PDFs from tour operators, you’re at the mercy of whatever structure someone else decided on. You can’t build intelligence on top of data you don’t understand or control.

And many companies aren’t just starting from zero, they’re starting from a deficit. Their existing systems are already inadequate. The booking engine is outdated. The product data is scattered across spreadsheets and PDFs. The customer journey has gaps everywhere. And instead of fixing those fundamentals, they think it is smart to layer an AI chatbot on top of it. As if intelligence on top of dysfunction creates something functional. It doesn’t. It just makes the dysfunction harder to diagnose.

You’re not solving a problem. You’re decorating one.

Building what we built required three things:

Owning our data. Every trip is ours. Visited by our team. Structured in our database. Embedded in our vector store. You can’t build semantic search over data you don’t structure yourself.

Owning our systems. Our own backend, frontend, deployment pipeline. When we needed a dual reranker, we built it. When we needed semantic conversation memory, we built it. When we needed an MCP server for Web 4.0 readiness, we built it. No vendor approval needed. No feature requests. No roadmap dependencies.

Owning the entire chain. From the moment a customer opens the chat to the moment they’re talking to a specialist, every step is ours. The AI knows our products because we made the products. The specialist knows the conversation because our system feeds it to them.

The AI is the last 10% of the work. The first 90% is building a company that can support it.

In Part 2, I’ll show you how it is actually running under the hood. The architecture, the search pipeline, the memory system that took many attempts to get right, and why 25 seconds of waiting can feel like 5 when you design it right.

Can a conversation be a travel channel? Part 2: The technical architectureThe technical deep-dive into how we built Joy: a RAG pipeline with vector search and reranking, a memory system that took four iterations, and the UI/UX decisions that make a 25-second response feel instant. All Subjects MatterRemco Loup.

Artificial Intelligence is here to stay, that’s not the topic here.

The problem is that everyone’s talking about AI, but few are actually thinking clearly about how to really use it. Somewhere along the way, “we use AI” became a marketing tagline, not a technical decision. Everyone wants to be part of the AI wave otherwise, you don’t belong. If you have to believe big tech the way they are forcing it down everyone’s throat.

But when you take a small step back, you’ll notice something strange:

Companies are solving problems with AI that they shouldn’t have in the first place.

Poor system designs, missing ownership, and spaghetti infrastructures are now being “fixed” by throwing LLMs on top of this pile of shit. And as a result, systems are becoming even harder to maintain, more expensive, and harder to reason about.

This blog isn’t about tearing down AI.
It’s about clear thinking, technical ownership, and using the right tools for the job.

OpenAI investigation research paper

Recent research by OpenAI and Georgia Tech highlights a core truth that often gets ignored: AI hallucinations, those moments where models make things up aren’t bugs. They’re a mathematical consequence of how these systems are built.

Even with perfect training data, the current architecture of generative AI rewards confident answers over admitting uncertainty. That means hallucinations are not only common, they’re inevitable, unless we fundamentally change how we train and evaluate these models.

As the researchers put it: “Hallucinations are mathematically inevitable.” (source)

How people abuse AI to solve the wrong problems.

Let’s take a step back.

If you’re paying $1,200/month for GPT-4 to parse email text, extract phone numbers, reformat JSON, and uppercase strings… you’re not doing innovation. You’re outsourcing common sense to an expensive black box.

Yes, someone recently did that, and realized the cost effect too late. They then moved to GPT-4o-mini and brought the bill down to $200…. But even then it still is a shit solution:

Those tasks didn’t need AI at all. Just basic software skills.

This isn’t an isolated case. It’s happening to much sadly.

People assume AI is their fix to everything, while it only shows the persons incompetence because:

• It increases costs
• Obscures what’s actually happening
• Introduces hidden dependencies
• Makes systems almost impossible to debug and maintain

AI is used to cover up a lack of system design, not to improve it.

AI as a Natural Language interface, powerful but misused

But let’s be fair: LLMs truely are powerful when used right.
They allow you to describe what you want in natural language, and get a result, without understanding the underlying logic.

This makes AI a fantastic tool for:

• Prototyping
• Interacting with large knowledge bases
• Summarizing unstructured data
• Performing fuzzy matching at scale

LLMs are essentially a new kind of interface, just like the command line, the GUI, or APIs were before.

But they’re not magic. And they are definitely not for free. And they’re almost always not the best way to solve a problem.

The more critical the function, the more important it becomes to understand:

• What is being executed?
• Where is the data flowing?
• Who owns the outputs?
• What are the real costs?

Conversational AI, the modern chatbot, but still a chatbot

There is a path forward for conversational AI. It’s just not the revolutionary leap people think it is with self thinking systems that wont hallucinate and always provide the right answer.

Yes, LLMs can provide smarter, more human-like responses then before LLM’s became mainstream.
Yes, they can automate support flows that were previously a lot more complex to script.

But let’s not fool ourselves:

People still want real, human contact with your business especially when something goes wrong.

If you outsource your entire customer service to an AI assistant, you’re sending a clear message (but the wrong one in my opinion) :

• “We don’t care enough to talk to you ourselves.”
• “You’re not worth the time of a real person.”
• “We only sell our product. dont come complaining to us please.”

This probably saves costs in the short term.
But long-term? You’re destroying the trust with your brand. Especially because people typically contact a customer service when:

• They’re frustrated
• They don’t understand something
• They have a complaint

That is not the time to offer them a chatbot, no matter how “smart” it sounds.

Great customer service is a relationship, not a cost center.

So yes, use conversational AI to assist your teams. But don’t let it replace human contact where empathy is needed. Dont make AI the interface of talking with your company for customers if you care about your product.

What you should be using AI for

Let’s flip it around in this section. What is a good use case for AI?

You should use AI when:

• It helps you do things that were previously too expensive, too slow, or technically too complex
• You’ve already optimized your architecture, but still have edge cases worth automating
• You’re enriching processes, not hiding technical debt

For example:

• Prototyping difficult functionalities so you can test outcomes quicker
• Enhancing product search with semantic understanding
• If you can make the hallucination visible in data they are perfect for BI (business intelligence)
• Generating tailored summaries for large internal reports
• Transcribing phone calls and make summaries
• Image to text so you can better semantic search on images in large image databases.

These are tasks where AI really shines, because they were even more expensive, hard or impossible to solve in traditional systems.

But again: always weigh the costs.
Not just in dollars, but in clarity, control, and customer experience. Always try to solve it without using AI first.

Tech maturity will determine who wins

Here’s the core of it all:

Companies with modern, understandable, well-documented infrastructure and data will win.
Those who keep layering AI on top of their chaos… won’t.

It’s not about how early you adopted AI. It’s about:

• Whether your stack is maintainable
• Whether your teams really understands it
• Whether you can reason about cost, performance, and failure
• Whether you own your data, your architecture, and your roadmap

The companies that treat AI as a new algorithm/interface to solve problems they could not solve before, will get the most long-term value in my opinion. Don’t use AI for it’s marketing value big tech wants you to.

Offloading data ownership is a strategic risk

Let me end with another real example I recently came across.

A company in the travel sector built an app for end-users to access their trip details. They are reselling this to other travel agencies and tour operators. That sounds feasible looking at the bad shape travel is in regarding IT.

So the travelagency can offer the app with all vouchers and itineraries for the trip of their customer. Nothing weird so far except you are offloading all your documents to a third party.

So now this itinerary app provider has access to a lot of your crucial business information. And now the customer buys extra services (e.g. insurance, transfers, upgrades) in the app. You should think this is upsell for the travel agency owning the customer.

Here comes the culprit. The platform suddenly gives commission to the touroperator… So is the app provider now also providing extra services to your customer??

Who owns the customer now?

It’s a major shift and a dangerous one. The more infrastructure you outsource, the more you lose control of your customer relationships and your first-party data.

Stay in control of the customer journey of your own customer. Protect your added value. dont become unity. And keep it personal.

This will become a huge issue! And AI will accelerate it if travel companies aren’t paying attention.

Conclusion: AI is a tool, not a strategy for most companies

AI is impressive. But it doesn’t replace fundamentals.

Don’t use it to paper over bad architecture.
Don’t use it as an excuse to avoid technical decisions.
Don’t let it obscure what your systems are doing, or who your customer really is.

Instead:

• Own your stack
• Understand your logic
• Use AI where it creates real, measurable value
• And never stop thinking for yourself

The real innovation isn’t in the tool. It’s in how clearly you think about the problems to solve.

For decades, businesses have adapted to the evolution of our internet. First came Web 1.0, a read-only internet where companies built static websites. Then Web 2.0 ushered in the interactive era—social media, user-generated content, and APIs that enabled apps to connect with services. Now, we are living in the Web 3.0 age, where AI enhances personalization, automates tasks, and understands context.

But this is just the beginning.

The next phase, Web 4.0, will introduce something even more transformative: AI as an autonomous digital agent. Instead of just assisting humans, AI will actively engage with businesses, making decisions, negotiating deals, and executing transactions—all with minimal human intervention.

I call this the autonomous web: a world where AI assistants don’t just retrieve information but act as independent digital consumers serving their Humans. And businesses that fail to prepare for this shift are at risk of becoming invisible in this generation of digital commerce.

The shift to AI-driven interactions

For years, businesses have focused on optimizing content for human users. Websites, apps, and APIs were all designed with the assumption that a person would be making the final decision. That assumption is now breaking down.

The evolution of digital interaction:

• Web 1.0: Static websites (businesses provide information, users consume it).
• Web 2.0: Interactive web (social media, APIs, mobile apps).
• Web 3.0: Intelligent web (AI-driven personalization, automation, and semantic search).
• Web 4.0: Autonomous web (AI agents acting as customers, negotiating, transacting, and making independent decisions).

We are now transitioning into Web 4.0, where AI agents will handle complex, real-time interactions—like booking trips, negotiating prices, and even dynamically adjusting services based on user behavior. Businesses that fail to adapt to this AI-first interaction model will simply be left out of AI-driven decision-making.

Why APIs alone are not enough

Many businesses might assume they are going to be prepared for AI-driven interactions because they have APIs. But traditional APIs were built for structured, predefined requests—designed for apps, not for AI agents that need context-aware, multi-step interactions.

This is where developments like the Model Context Protocol (MCP) comes in.

MCP will be one of the missing links that allows AI assistants to interact with businesses as if they were human customers. Unlike traditional APIs that return static responses, MCP allows for:

• Dynamic service requests (AI can ask for specific customizations).
• Negotiation and refinement (AI can modify criteria based on preferences and constraints).
• Autonomous decision-making (AI can finalize a transaction without human confirmation).

The companies that adopt AI-native interfaces will dominate the Web 4.0 economy. Those that don’t will be bypassed—not by people, but by AI-driven agents making purchase decisions for their humans.

A fully AI-driven transaction at 333Travel

At 333Travel, we don’t sell pre-packaged vacations. Every trip is tailor-made, requiring back and forth interaction to fine-tune your perfect trip. Today, this process happens through web forms, email exchanges, and phone calls. But in Web 4.0, this will look very different. and all these channels will be combined together with talking to an AI assistant as an option.

A new kind of travel interactions

Imagine a traveler, Lisa, who wants a customized trip to Thailand. Instead of visiting a website, filling in a form, or calling an agent, she simply tells her AI assistant:

“I need a vacation, Find me a two-week tailor-made trip to Thailand with a mix of luxury and boutique hotels, some adventure activities, and time for relaxation. somewhere mid june.”

Lisa never opens a website. Her AI assistant does the work for her.

Step 1: The AI makes an intelligent request

Lisa’s AI contacts 333Travel’s MCP-enabled interface through a search, sending a structured request with her preferences, past travel history, and budget constraints.

Step 2: AI and 333Travel’s system negotiate and refine the trip {#step-2-ai-and-333travel%E2%80%99s-system-negotiate-and-refine-the-trip}

Since 333Travel specializes in tailor-made experiences, a real-time negotiation process begins between Lisa’s AI and 333Travel’s MCP integration:

1. AI sends initial request
   • “User prefers Thailand, mix of luxury and boutique hotels, some adventure, some relaxation.”
2. 333Travel’s MCP responds dynamically
   • “Recommended itinerary: Bangkok, Chiang Mai, Koh Samui.”
   • “Luxury options: The Siam Bangkok, Anantara Chiang Mai, Four Seasons Koh Samui.”
   • “Boutique options: Sala Rattanakosin, Tamarind Village, The Library Koh Samui.”
3. Lisa’s AI refines the request
   • “She has stayed at Four Seasons before and rated it highly—prioritize that.”
   • “She prefers cultural experiences—add a food tour in Bangkok and a temple visit in Chiang Mai.”
   • “Budget cap: €5,000—adjust accordingly.”
4. 333Travel then creates a personalized proposal based on all the information normally received in a phone call.
   • Three itinerary options are discussed with Lisa’s AI, ranked by price, luxury level, and activity balance.

Step 3: The AI confirms and secures the booking

Lisa herself checks the summary and says:

“Book the second option and add an airport transfer.”

Her AI assistant:
✔️ Finalizes the booking with 333Travel
✔️ Secures payment automatically
✔️ Sends confirmation to her email and calendar

Lisa never interacted with a website. Never sent an email. Never called a travel agent.

The autonomous web can handle everything, but Lisa can step in at any moment and decide to make that phone call.

The Web 4.0 shift will completely change how businesses engage with customers.

What this means for businesses

The key takeaways:

1. AI-driven transactions can become the new norm

If a business isn’t optimized for AI-first interactions, it won’t be part of AI-driven purchase decisions.

2. AI optimization will replace traditional SEO

Just as businesses optimized for Google search rankings, they will now have to optimize for AI-driven discovery—ensuring their services are structured for AI agents to understand and interact with.

3. AI-native interfaces will be the next competitive advantage

Companies that create AI-ready endpoints using MCP-like protocols will likely dominate the Web 4.0 landscape in the early stage.

4. Customers will expect frictionless experiences

If an AI can request an entire itinerary in seconds with detailed information, why wouldn’t you make use of it, and combine it together with the normal human interaction. You just created an extra sales funnel next to your ability to Mail, send a text message, visit a website or make a phone call.

The autonomous web is here. Are you ready?

Web 4.0 isn’t something happening in the distant future—it’s happening right now. AI is no longer just assisting customers; it’s acting on their behalf.

The businesses that prepare today will be ready at the start of the Web 4.0 economy. Those that ignore it will simply be bypassed—not by human customers, but by their AI-driven agents.

The question is no longer if AI-driven customers will reshape business—it’s whether you’ll be ready to serve them when they do.

Why 333travel sets the travel standard in an AI-driven world

The world is changing rapidly. With the rise of AI models like ChatGPT, ClaudeAI, and Gemini, access to data and insights is easier than ever. Even companies with no real expertise in travel try to enter the market by leveraging the product of someone else with technology only. But what sets 333travel apart from these “digital dropshippers”? Simple: our own technical knowledge, our passion, and FULL control over our OWN product, as you are going to read below.

We’re not just a travel agency; we are also an IT company built on expertise and reliability.

At 333travel, we combine people and technology, innovation and authenticity, and remain the undeniable choice—even in a world where AI is becoming more visible every day.

Generic AI vs. self-trained AI

Not all AI is the same. Public AI models, like the ones mentioned above, provide standard solutions trained on publicly available data. This makes them accessible and allows them to function as a new sales channel. We ensure that these generic AI solutions ultimately come to us for their travel needs—because of our proven expertise.

But we already took it quite a few steps further. We’ve already been exploring extensively how self-trained AI, powered by our unique company data, can optimize and accelerate our internal processes. One thing is clear to us: AI does not replace our DNA, personal touch, or customer care. It’s a fantastic tool, a “smart notepad” that supports our specialists. Whether it’s streamlining administrative tasks, performing faster analyses, or assisting with complex inquiries—it allows our travel experts to focus on what truly matters: personal service and customer satisfaction.

Because the balance between AI and human interaction is crucial to us, we will always make sure to keep the human touch at the heart of our customer relationships. Travel is about emotions, passion, and real connections—something no AI will ever fully replace for us.

AI as a catalyst, not a replacement

AI is a powerful tool, but it wil not replace our expertise and experience. While some companies leverage AI to create generic travel offerings on the data of others, we remain the trusted point of contact where both humans and AI assistants book their trips. Why? Because even AI, when aiming for the best outcome for its user, recognizes that 333travel sets the standard for complete, worry-free travel experiences and unique up to date information.

What makes us stand out:

Up-to-date knowledge: Our specialists have personally visited the destinations, knowing more than what’s just available online.

**Local Partnerships:**In each country we offer, we have our boots on the actual ground. We work closely together with our local partners to stay on top of our game. It wont matter which country you visit with us. We will always know best how to assist you due to the fact the whole trip is also handled by us.

Full control: Our in-house IT capabilities allow us to operate independently. We manage our own data, develop our own systems, and can even train our own AI models with our unique expertise. IT is deeply embedded in our DNA—unlike many other travel companies that rely on third parties.

**Passion for travel:**Our trips are not just logically put together; they are designed to make dreams come true.

We make sure you can enjoy your journey without worries

The risks of “digital dropshipping” {#the-risks-of-%E2%80%9Cdigital-dropshipping%E2%80%9D}

In this AI-driven world, some companies offer travel products without truly understanding them, simply by smartly combining data. This approach is similar to dropshipping—selling third-party products without real knowledge or control. But how can you create real value without deep expertise in your product?

Imagine assembling a kitchen by ordering each part from a different supplier. It might sound cheap and efficient, but what happens if the parts don’t fit? Who is responsible when something goes wrong? At 333travel, we ensure that these kinds of issues never arise. We provide a seamless, complete product with full accountability and peace of mind, so you can enjoy your journey without worries.

Why 333travel is the best choice—even for AI {#why-333travel-is-the-best-choice%E2%80%94even-for-ai}

In the future, AI assistants will increasingly recommend trips based on data. But even AI will soon recognize that a successful trip depends on more than just raw data. At 333travel, we deliver an experience that AI can recognize—but cannot create itself. AI needs us to handle it and book it for them.

Unique destination knowledge: We understand the nuances and details that no database contains.
**End-to-end service:**From the first inquiry to post-trip support, we are always there for our customers.
Reliable data and systems: Our own cutting edge IT solutions and data ensure everything is accurate, up-to-date, and executable.

AI will not book a trip with a company that poses risks or offers generic solutions.

Even in the future, AI will choose security, quality, and reliability—which means choosing 333travel.

How AI strengthens us

We don’t see AI as a competitor, but as a tool. At 333travel, we use technology to streamline our processes and enhance our service, while keeping our focus on human expertise. Some examples:

Efficient processes: AI helps us recognize patterns and trends faster, allowing us to work smarter and provide better answers to our customers.
Enhanced travel proposals: Our own data and knowledge ensure every proposal is personal and unique—AI speeds up the process but always relies on our self-written content.
**Improved communication:**Chatbots and AI assistants support our team in the background, giving us more time for direct, personal contact with travelers.

What makes us unique is our passion, authenticity, and human approach—combined with our fully in-house IT backbone, which now more than ever sets us apart from the rest.

To be truly innovative, you need full control over your own business-critical processes.

The future of travel: 333travel as the undisputed choice

The travel industry is evolving. Technology will play an even greater role, but the core remains the same: the desire for unique, unforgettable experiences. At 333travel, we combine the best of both worlds—the power of technology with the warmth of human expertise.

The biggest challenge in the travel sector lies with companies that depend on third parties for AI implementations, or those that claim to “own” data while merely repackaging generic AI results.

We position ourselves as the central contact point—for both people and AI assistants—by consistently delivering the highest quality. Whether you dream of a tropical beach or a cultural adventure, you can trust that with us, you’re in good hands. We manage the entire chain, from data analysis to personalized service, ensuring that your trip is not only perfect but also worry-free and unforgettable.

Conclusion

AI is changing the game, but at 333travel, we continue to stand out. Our unique combination of up-to-date knowledge, passion, and full control over our product makes us the go-to choice for any traveler. Whether you reach us through personal contact or via an AI assistant, one thing is certain: with 333travel, you’re not just booking a trip—you’re securing a complete experience.

So, how do you stay unique in a world increasingly driven by technology? We do it by continuously improving our data, leveraging AI in smart ways, and ensuring that travelers still come to us to turn their journeys into reality—just as before.

As someone interested in the ever-evolving world of technology, I’ve witnessed a lot of developments in AI, particularly in the realms of language processing and user interaction. Reflecting on these advancements, I’ve been wanting to share why, in my opinion, the recent strides in AI aren’t as surprising as you might find them to be.

I use different pronounces as i refer to the same thing. I use AI and LLM (Large Language Model) for todays take on systems like ChatGPT.

My first Encounters with “AI”

My journey with LLMs began earlier than some people realize. I had my first experiences with Flow.ai around 2018/2019. Back then, it was clear to me that we were on the brink of something. However, the general public seemed largely unaware of these undercurrents in AI development. The fact is, companies like Flow.ai (now part of Khoros) were laying the foundation for AI systems we see today like ChatGPT.

Read up on a post i wrote back in 2019 about our experiences with Flow.AI together with Stefan Samba.

2019 - Blast from the past: How 333travel is revolutionizing the Travel industryDiscover how 333travel’s AI ‘Joy’ redefines customer interaction, blending AI’s intent understanding with personal touch. A game-changing journey in AI-led travel solutions! All Subjects MatterRemco Loup.

Flow.ai: A Missed Opportunity for 333travel

My experience with Flow.ai was an eye-opener back then. It was a game-changer in the AI space for me, demonstrating the potential of neural network-based conversational AI. Looking back at it, when we tried integrating this technology at 333travel back in 2018, we might have been to far ahead of our time. Our natural style of helping people with asking for a proposal, did not gain the traction we hoped for using a LLM. Our customers (luckily?) preferred interacting with human specialists rather than bots. This was a clear indication that while the technology was promising, market readiness was another story.

People hated talking to the “oldstyle” chatbots not understanding what you meant due to the lack of intent understanding.

The Siri example

Let’s talk a bit about Siri, Apple’s virtual assistant. Since its launch in 2011, Siri has made some strides. However, from my point of view, these improvements have been modest at best in the intent learning space. Siri, along with other tools like Cortana and Google Assistant, still required very specific commands and often struggled with understanding intent. While there has been some progress there, it’s been to slow of a curve in understanding human language and mainly intent.

• 2010-2011: Siri’s initial years saw its integration into the iPhone 4S, offering basic voice recognition and command execution.
• 2012-2013: Expansion into new languages and dialects, broadening its user base.
• 2014-2015: Entry into new markets and the launch of SiriKit, allowing third-party app integration.
• 2016-2018: Introduction to macOS and the release of Shortcuts for customized voice commands.
• 2020-2021: moderate improvements in language translation, integration with Apple’s ecosystem, and enhanced speech recognition.

AI and Customer Interaction

As a technology enthusiast, I find ChatGPT impressive in many ways, but it’s essential to acknowledge that it’s no substitute for the detailed understanding that human specialists provide. Lets take a Tour Operator for example. Crafting a feasible travel plan is a whole different take. The last thing you want is to make mistakes there.

So why would we let an AI do the talking to the customer and then replace it with a human? Isn’t it better to do it the other way around? Let our specialists use an AI to create a personal travel plan and send it quicker using AI? Bonding your customer with real human interaction is most preferable when doing sales.

If you dont know what you are talking about you are not able to spot mistakes in responses from AI. This will always be a major concern. First hand knowledge is still mandatory. That is why something like ChatGPT is a tool that needs to be used by skilled people about the subject to accelerate their work.

I dont believe in separate integrations within websites that take up space pretending to be a smart AI. The search function already present on websites will solve this issue. and people that are going to use an AI (LLM) dont want to interact with a different LLM every time. they will use their own PA to answer these questions if needed.

The first step companies need to solve is implementing a search engine in their website that understands intent. By understanding intent you can give people actually a useful answer based on the content on your website.

You need a profound integration with your LLM and your rich content. Especially in the Travel Industry. Using your website to become smarter using LLM is much more logical in my opinion then a popup “person” you can talk to.

The future of AI is undoubtedly exciting, but it’s a path we must navigate with both optimism and caution.

My Take on AI’s Future

Looking ahead, I see AI as a field growing with possibilities but also filled with challenges. The journey from the early days of specific command-based systems to the more intuitive, context-aware models from today have been fascinating. However, there is still a long way to go in achieving the of AI that can really understand and interact with humans on a more profound level.

Conclusion

In summary, while AI’s recent advancements may seem sudden and surprising to many, they are part of a long, ongoing process of technological evolution. My experiences at 333travel, especially with innovative platforms like Flow.ai, have shown me the potential and limitations of AI. As we move forward, I believe it’s crucial to balance our enthusiasm for AI with a realistic understanding of its current capabilities and limitations.

You are unique by keeping it close to yourself and not by using technology to do your core business for you. Always keep a personal touch!

Here is the link to the original article with the waybackmachine: Due to covid and flow.ai beeing taken over by khoros this blog is no longer publicly available. This blog was written in q3 of 2019.

https://web.archive.org/web/20201020201705/https://flow.ai/blog/how-333travel-is-revolutionizing-the-travel-industry

Sadly we have chosen to retire Joy due to the lack of results in early 2022 and Covid hitting us hard. The customer engagement was to low to keep it alive sadly.

Artificial intelligence (AI) to keep in touch, in a personal way, outside opening hours. That is what 333travel uses their chatbot they called Joy for.

The travel industry is an innovative market. The competition is prevalent and the urge for technological advantage is great but also difficult. If you do not operate technically independently in this market, there is a good chance that you will be absorbed by the crowd too much and can therefore no longer be unique in your proposition and your right to exist will be endangered.

We are constantly looking to improve our position on the market and every day we translate IT into the business in the right way so that IT actually works for us. Keep experimenting and keep on trying new things!

Our challenge

We are always looking for ways to better serve our customers and we want to do this in a personal way. This way we can really build a relationship with our customers and offer fully customized solutions.

We are convinced that when you travel with us you will get the best possible experience of enjoyment because at 333travel we think along with you from A to Z and completely unburden you.

…With the help of Joy the customer has their answer faster and we can offer more quality because Joy indirectly ensures less work and more available information before a personal contact moment takes place…

The use of artificial intelligence may therefore sound contradictory, but that is certainly not the case. We will of course start to use this AI to provide our customers with the existing information / FAQ on our website if they have any questions about it.

But in addition to providing information, we will always try to direct to personal contact. It is important for us to physically come into contact with our potential customers, in a more personal, easy and accessible way.

Our goal

The automated collection of leads in a way that is as personal as possible is therefore our case for the implementation of this artificial intelligence. The rest is a added bonus. We strive for even more for personal contact through this AI. The focus is creating value and optimizing our customer journey outside of our regular working hours.

Our solution

Together with Flow.ai, we have started testing to see what personal ways we could use to get a customer to get in touch with us.

A correct use case of AI is very important here, but also the way in which you can train this AI. We wanted to create the most natural way of interaction, but we have to work with non sexy data such as “departure” and “arrival” dates to get an idea beforehand of when someone wants to leave for his holiday, this is a greater challenge than it seems.

We absolutely wanted to avoid that our AI does not immediately understand it when someone says “I want to visit Thailand on June 17 for 1 week”.

A natural interaction with the right intent

We have therefore trained our LMM that it understands intents with knowledge of what the departure date is and when you want to leave. It is therefore not an absolute fact what we want from you, but it is a value that falls within the meaning of “when do you want to leave”. There are so many different but also correct answers to this question and if you take our requirements into account, this bot must also understand everything below and more:

• In the summer i want for the holidays…
• During the winter i would like to stay …
• In autumn I want to go to…
• I want to be in Thailand for Christmas…
• We want to go to … next year

It immediately becomes unfriendly if after this question there is still a date field to be filled with when do you want to leave. Or even worse, the bot dont understands the question at all. a customer also often does not know when he wants to leave exactly. When he starts like this, is he obliged to fill in this information? You have one try with this type of customer.

Customers are fed up enough already with non working chatbots from large companies trying to reduce FTE and see how far they can go to annoy their customers.

We instantly record the key information of the expected departure period automatically. After all, this will be deepened further in the contact moment, for now our specialists will know what the intent is and can act accordingly.

It is also possible within our implementation to capture all the variables we need with only one sentence from a customer. We only need the contact details after this and we give a call on the requested time and date.

Collecting data

“Good afternoon, I am looking for a holiday starting in Indonesia and ending in Bali for 2 adults and 2 children with a hotel on the beach leaving July 21 and coming home the 3th of August .”

The above sentence is a practical example of what Joy understands. We record these values and we immediately know that there is enough data. We only ask for the customer’s telephone number/e-mail address and call him at a preferred time specified by himself.

we call you at the preferred moment, we consciously chose not to let the customer choose something through a date picker to keep the natural flow. For the customer it is easier to say: “next Thursday at 10 am”. From this, we understand what the intent is and call that particular customer at that exact time. There are also plenty of customers who say they want to be called right now. Regardless of whether they can be called at that moment or not, they drop out as soon as they get a date picker presented. Especially when the present day is missing.

Because the customer visits our website, we also know from which page we have received his request for personal contact. This way we have even more information for our specialists to get an idea of what the intent is.

This data gets send automatically as a lead/proposal in our backoffice called BOSS. And every follow up on this lead is recorded there.

Result: Joy sees the light.

After a large number of internal tests, various tweak adjustments and using various methods to achieve our goal, Joy was born. We have also given her a personal face because of our intention to keep the conversations personal. Of course, we do indicate that Joy is a “Virtual Travel Assistant”. Our real colleagues are still the travel specialists.

…What is particularly noticeable from the results is that the interactions and requests that come in via Joy are usually from people between 20 - 35 years old…

Important lessons

The most important lesson in this might be a bit of an open door, but make sure your goal is set on paper, and start with the simplest implementation of this goal and slowly develop it into a more complex working final product.

We first started to look at ways in which we can collect our required data with Flow.ai and really understand how this works. Then we tested a lot of different scenario’s and slowly worked towards one complete flow that combined the best of multiple flows together. We immediately gained a lot of experience with the various options that flow provided this way.

We are well supported in this by Flow.ai, but due to our own in-house knowledge, we didn’t require the support all the time and could really dive deep on the subject.

The flow.ai system is well structured, very logical and understandable. This shouldn’t block you from experimenting.

The future

The first Quarter is our busiest period. Because we implemented Joy relatively shortly before this period, we have not yet activated the option to Live Chat. This is something that is going to be activated after this quarter.

In addition, we want to go even further in the near future with Joy and make her THE assistant of our customer portal that will help you on your way after you made a booking. If Joy does not know the answer, you will immediately get in touch with the specialist you booked the trip at. If you have any questions during your trip? Ask Joy. Do you have problems during your trip? Joy ensures that you get the answer as soon as possible and that you get into direct contact with us when needed.

We see Joy as our “Virtual Travel Assistant” where the very specific knowledge of our destinations and our knowledge of customization remains with our travel specialists because this is the strength of our personal contact in which we personally make the difference, within our philosophy.

Introduction

I am already for a while intrigued by transforming a business into a DAO (Decentralized autonomous organisation). Thinking of my role as a Product Owner i thought of a way that can work very well when transforming a part of our business into a DAO. We can benefit from Scrum, Agile and leveraging a DAO altogether this way.

In my personal case below, a hybrid approach is preferable, where certain aspects of our business are managed by a DAO, while the rest remains under traditional management for now. My DAO would be used to manage our Front/Mid/Backoffice system called BOSS to make decisions of what to develop next, while the broader business continues to operate in a more conventional manner. This approach allows our business to benefit from the advantages of a DAO, while mitigating some of the risks and challenges.

Our goal is to develop this system into a SaaS product within the travel industry. And use a DAO for example to get our stakeholders involved. But you will read more about my idea if you carry on.

What is a DAO?

a DAO, or Decentralized Autonomous Organization, is a type of organization represented by rules encoded as a computer program (called a smart contract) that is transparent, controlled by the organization members, and not influenced by a central government.

A DAO’s financial transactions and rules are maintained on a blockchain, which is a type of distributed ledger. This makes it completely autonomous and independent from any external or centralized control.

Imagine it like a digital company, where all the rules and regulations are coded into a smart contract. For instance, let’s say there is a rule in a DAO that a certain proposal requires 60% of votes to pass. This rule is coded into the smart contract, and the blockchain will ensure that this rule is strictly followed, and can’t be manipulated or bypassed by anyone.

Members of a DAO can make decisions about the rules or direction of the organization by proposing and voting on proposals, each having a vote proportional to their stake or shares in the DAO. In this way, a DAO is a bit like a traditional company but managed in a fully democratic and decentralized way.

To sum it up, a DAO leverages blockchain technology to ensure that the organization is governed in a completely transparent, democratic and decentralized way, free from any central or external control.

Tokens

Creating a unique token for a Decentralized Autonomous Organization (DAO) can be beneficial, and is a common practice, whether it’s the “best” practice depends on the specific goals and context of the DAO. Here’s why DAOs often issue their own tokens:

1. Voting Rights: Tokens can be used to represent voting power within the DAO. The more tokens a member holds, the more influence they have over the DAO’s decisions.
2. Incentives: Tokens can be used to incentivize certain behaviors or contributions to the DAO. For example, tokens can be given as rewards for completing tasks that benefit the DAO, such as coding, designing, or managing projects.
3. Fundraising: Issuing tokens can be a method of raising funds. Potential participants can purchase tokens, effectively becoming investors in the DAO.
4. Community Building: Having a unique token can help to build a sense of community among members. It can foster a feeling of shared ownership and mutual investment in the DAO’s success.

However, there are also challenges and considerations to keep in mind:

1. Regulatory Issues: Depending on the jurisdiction, issuing a token might be considered as issuing a security, which can have regulatory implications and legal requirements.
2. Token Distribution: Deciding on how to initially distribute the tokens can be challenging and controversial. The distribution method needs to be fair and align with the DAO’s goals and values.
3. Token Value Fluctuation: If the token is traded on the open market, its value can fluctuate, which can lead to instability and potential manipulation.
4. Complexity: Issuing and managing a token adds complexity to running the DAO. Not every DAO might need its own token, especially if its operations are simple or if it can effectively use an existing token.

How to leverage a DAO as a Product Owner

Imagine running a Decentralized Autonomous Organization (DAO) where your customers or interested parties are able to invest directly in your product. They’re granted stakes or tokens, which gives them a voice in your development process which is run in a Scrum/Agile way.

Let’s say these potential customers want a specific function, X or Y, added to your product. They can cast votes for that function using their tokens, directly influencing the development priorities. This voting mechanism can draw potential customers closer to your brand and empower existing customers to shape the product they use regularly.

In effect, the features that top their wishlist become the focal point of your development strategy. The beauty of this approach lies in its transparency. By allowing your users to vote on potential additions to your product, you’re showing them why certain features are prioritized over others. It’s a democracy where voting requires knowing the “why” behind each decision, leading to a detailed understanding of product evolution.

This approach is a boon not only for Product Owners but also for your business at large. Instead of cloaking your development process in secrecy, you’re inviting your users to be part of it. This participative approach can foster faster trust-building among customers and potential users than traditional methods.

As a Product Owner, you’re still essential in filtering and interpreting customer wishes, converting them into comprehensible stories that both technical and non-technical folks can grasp.

When it comes time to select top priorities, your roadmap is already outlined by the democratic vote. This interaction allows you to engage and learn from your customer base quicker than relying on personas or other traditional tools to decide what comes next. Majority votes could define your sprint goals, guiding your focus for the next development cycle.

What better way to foster transparency and customer input using Agile/Scrum in this way? This process integrates the principles of adapt, inspect, and learn in a tightly-knit manner. With a DAO-like approach, your product evolution truly becomes a community-driven endeavor.

Caveats

There are also caveats of course.

1. Governance Challenges: Implementing a DAO’s governance model can be complex. Balancing the interests of all stakeholders in a fair and equitable manner is a significant challenge. It’s not always the case that the majority’s decision aligns with the best interest of the project or all the stakeholders.
2. Smart Contract Security: DAOs operate on the basis of smart contracts, which are immutable once deployed. If there’s a bug or vulnerability in the contract, it can lead to severe issues, including loss of funds. The infamous “The DAO” hack of 2016, where $50 million was siphoned due to a smart contract loophole, is an example.
3. Regulatory Uncertainty: DAOs exist in a legal gray area in many jurisdictions. How they’re classified and regulated by law is still uncertain in many parts of the world.
4. Token Distribution: How tokens are initially distributed can also be a complex issue. If not done properly, it can lead to centralization or imbalance of power in the DAO.
5. Low Participation Rates: DAOs rely on active participation from token holders. However, voter apathy can be a significant issue, leading to decisions being made by a small subset of stakeholders.
6. Short-Termism: There’s a risk that token holders who are only interested in short-term profit might vote for proposals that yield immediate gains but are detrimental in the long run.
7. Interface between DAO and the Real World: DAOs are digital, but their decisions often have implications in the real world, where enforcement can be more complex.

Each of these challenges is surmountable, but they do require careful thought and design to address effectively. As with any system, the benefits should always be weighed against the potential risks and drawbacks.

The role of a Product Owner

This is where a good Product Owner comes into play and lets people not vote on your whole backlog but on clearly selected items that are already enough refined and ready to be developed to a degree of uncertainty. You are still supposed to know the overall market so you can preselect the items to be worked on and let people vote on them.

You still have stakeholders to keep happy and develop functions the market wants the most. Only by using a DAO and keeping people engaged you can adapt and learn your market way better then in other ways.

Even though a DAO is fundamentally democratic, it needs someone with a comprehensive understanding of the product to guide its development. A PO can offer insight into the feasibility and impact of features, helping to steer decision-making and prioritization.

Smart Contracts

A smart contract is a computer protocol that facilitates, verifies, or enforces the negotiation or performance of a digital contract. Smart contracts run on the blockchain, making them transparent, traceable, and irreversible. They’re designed to automatically execute transactions or other specific actions when pre-set conditions are met.

Here are some key reasons why it’s important to carefully design and implement smart contracts:

1. Immutability: Once a smart contract is deployed on the blockchain, it cannot be altered or corrected. This immutability makes it crucial that a smart contract is thoroughly tested and reviewed before deployment to ensure that it operates as intended.
2. Security: Smart contracts are vulnerable to bugs and exploits if they aren’t correctly programmed. Given the value often transacted or managed through smart contracts, security is of the utmost importance. Exploits can lead to substantial losses, as seen in the infamous DAO hack in 2016 where around $50 million was lost due to a bug in the DAO’s smart contract.
3. Financial Implications: Smart contracts often manage, store, or facilitate the transfer of cryptocurrency, and errors or vulnerabilities in the contract could have significant financial repercussions.
4. Trust and Reliability: The trust in a smart contract, and by extension, the entire operation or system it supports, heavily relies on the contract functioning as expected. Issues could compromise the perceived reliability of the system and discourage participation or usage.
5. Complexity and Interoperability: Smart contracts often interact with other contracts or systems. It’s important to think through these interactions to prevent unexpected behavior. Misunderstanding or neglecting the complexities of such interactions can lead to detrimental consequences.
6. Regulatory Compliance: Depending on the jurisdiction and the specific use case, smart contracts need to be designed to comply with relevant laws and regulations, adding another layer of complexity to their design and implementation.

Therefore, the design, implementation, and review of smart contracts need to be taken very seriously and conducted with utmost diligence. In many cases, it’s advisable to have smart contracts audited by a specialized security firm before they are deployed.

A written sample of a Smart Contract

So you need a good working contract on beforehand. The Product Owner can be the manager of such a smart contract for example. He adds the issues to the system. and the stakeholders can vote on these issues using their tokens.

Each vote raises the importance of the issues to be developed. After which you pick the highest ranked issues to develop on while keeping an eye of what you can work in a single sprint ofcourse.

You can also use two types of tokens for example. Normal tokens for your customers using your product and social tokens for potential customers for example.

Validity of tokens is also something to consider since wishes can become obsolete after time.

This all can become quite complicated and i might post a full sample of a smart contract written in Solidity in a later blog using the conditions above.

Conclusion

This has become a somewhat longer article then usual but it needed a lot of explanation to be able to understand what it means to leverage your role as a PO inside a DAO.

I hope this has helped a bit to clarify and explain what a DAO is and how it can be used especially from a SCRUM/Agile Product Owner point of view where both can really thrive.

Francois Fort wrote a article about this on scrum.org Are DAOs the future of Agile Organisations.

You need stakeholders to buy in and what is better then to have engaged stakeholders with stakes in your product leveraging a DAO? You get detailed and immediate responses from the market when you have a engaged diversified group of people wanting your product to succeed because they then also succeed .