For most travellers, the anxiety of booking a flight starts before the airport. A fare appears, the dates work, and then comes the harder question: can it be paid for today?

Laters.com, the Singapore-founded online travel agency previously known as Fly Fairly, is trying to build a business around that moment. The company has raised US$1.5 million in seed funding led by XBO Ventures, the investment arm of digital-asset platform XBO.com, while completing its rebrand from flyfairly.com to laters.com.

This round comes over a year after the firm acquired LFG, a social travel discovery engine known for its viral, Gen Z-focused user experience.

Also Read: What travel tech can look like for the travel industry’s revival

The startup sells flights across more than 650 airlines and lets customers pay through over 100 methods, including digital wallets, cryptocurrencies, and around 40 buy-now-pay-later and instalment options. Its pitch is not that it has cheaper fares than everyone else, but that it gives travellers more ways to complete a booking once they find one.

That distinction matters in Southeast Asia, where online travel demand has returned strongly after the pandemic, but payment behaviour remains fragmented. Credit card penetration varies widely across the region, while wallets, bank transfers, instalment products and local payment rails often dominate daily spending. For travel platforms, that creates a gap: demand may exist, but checkout can still fail if customers cannot use their preferred payment method.

Laters.com says the majority of its payment volume is already non-card, which it describes as the inverse of much of the wider travel industry.

“Nobody should lose the fare they found because payday is two weeks away,” said Alex Yardley, founder and CEO of Laters.com. “Family, work, a wedding: some trips cannot wait. Laters.com fixes the price today and spreads the cost, so the people who plan ahead are not the ones who pay the most.”

From Fly Fairly to Laters.com

Laters.com launched from Singapore in August 2024 and says it has been profitable every month since February 2025. The company claims it is now on a run rate of more than one million travellers a year searching for flights on its platform. The United States has become its largest market, despite the company being headquartered in Singapore.

That geographic mix hints at the nature of the product. Buy now pay later, or BNPL, is well established in markets such as the US, Australia and parts of Europe, but Southeast Asia has also become an important testing ground for alternative payments. Players such as Atome, Kredivo, Grab, Shopee and others have trained consumers to split purchases into instalments, even as regulators keep a closer eye on consumer debt and transparency.

Flights are a particularly sharp use case because prices can move quickly and the ticket size is often much higher than a typical e-commerce purchase. Laters.com says customers using local and flexible payment methods book 35 per cent more often and spend 24 per cent more per booking than card users, citing a Stripe case study. It also says BNPL customers generate an average order value more than three times higher than card users.

Those figures explain why travel companies are paying closer attention to checkout design. In a market where customer acquisition costs are high and margins can be thin, a failed payment is not a minor technical issue. It can be the difference between a booked trip and a lost customer.

Crypto at checkout, not as a gimmick

The other part of Laters.com’s proposition is cryptocurrency. The platform accepts stablecoins and more than 70 other cryptocurrencies, settled at checkout like any other payment method. According to the company, crypto customers spend more than twice as much as the average customer, typically on long-haul trips and higher cabin classes.

Also Read: The unsexy side of SEA traveltech: eSIMs, visas and hourly hotels win big

That does not mean crypto has become a mainstream way to buy airline tickets. In much of Southeast Asia, digital assets remain volatile, unevenly regulated and often associated more with trading than day-to-day payments. But stablecoins, which are designed to track the value of fiat currencies such as the US dollar, are increasingly being watched by fintech firms as a possible bridge between crypto users and practical spending.

For XBO Ventures, that appears to be the investment thesis.

“We back companies that turn digital assets into something people actually spend. Laters.com has done that at one of the sharpest points of friction there is: paying for a flight,” said Dor Maman, co-founder and CFO of XBO. “The majority of its volume already moves outside the card networks, and that is where we think this market is going.”

XBO Ventures has also backed payments infrastructure company Rapyd, participating in its US$500 million Series F round, according to the company’s notes to editors.

A crowded field, but a narrower wedge

Laters.com sits at the intersection of several competitive categories. In online travel, it faces large platforms such as Booking Holdings, Expedia Group, Agoda, Traveloka, Trip.com and AirAsia MOVE, many of which already offer flights and have deep supplier relationships. In flexible travel payments, companies such as Alternative Airlines have long promoted instalment options for flights, while Travala is known for allowing crypto payments across travel bookings.

In Southeast Asia, superapps and local travel platforms also have the advantage of distribution, loyalty programmes and embedded wallets.

Laters.com’s narrower wedge is payment breadth: instead of competing mainly on inventory or price, it is trying to become the travel checkout that adapts to how younger consumers already pay elsewhere.

Yardley’s background also reflects that mix of travel and commerce. Before founding Laters.com, he spent two decades across travel and e-commerce, including roles on eBay’s EMEA leadership team, as Senior Director at Booking Holdings leading partnerships for Agoda and Booking.com across Asia Pacific, and most recently as Managing Director at ShopBack.

The company is also publishing two free tools aimed at travellers navigating the fragmented pay-later market. One is a country-by-country guide to fly now pay later providers, covering options such as Klarna, Afterpay, Zip and Atome. The other is the Laters.com Payment Score, which rates pay-later providers available at checkout out of 10 based on repayment flexibility, interest rate, eligibility and approval process.

Laters.com says no provider pays to be scored, ranked or placed, and that it earns the same margin regardless of how a traveller pays. That claim will matter if the platform wants to be seen as a neutral guide rather than another checkout funnel.

The new funding will go towards brand growth, expansion of the core flight business, and new products and verticals. Yardley hinted that the company’s ambitions may stretch beyond travel.

Also Read: Travel is back, and it’s more cutthroat than ever

“Millennial and Gen Z travellers have spent a decade adapting to a booking experience built for their parents,” he said. “We built Laters.com around how this generation actually pays, and it turns out what we built works well beyond flights.”

For now, Laters.com is making a focused bet: that the next wave of online travel growth will not only come from more destinations or cheaper fares, but from giving travellers more control over how and when they pay.

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Southeast Asia’s Native AI companies raised US$4.1 billion in the first seven months of 2026, more than double 2025’s full-year total, according to Tracxn’s Southeast Asia AI Startup Landscape report.

On a headline chart, that looks like a region riding the same AI wave as everyone else. Strip out a single transaction and the picture changes completely: Kling AI’s US$2.8 billion Series D alone accounted for roughly 68 per cent of that total. Take it out, and Southeast Asia’s AI companies raised closer to US$1.3 billion, and the number of disclosed rounds fell from 41 in 2025 to just 23 this year.

Also Read: AI is eating the world and startups are riding the infrastructure wave

Bigger cheques, fewer of them, concentrated in fewer companies. That is not a funding boom. It is a filtering mechanism, and most of the region’s AI “wrapper” startups (the ones offering a thin, prompt-engineered interface over someone else’s foundation model) are on the wrong side of the filter.

The wrapper reckoning is already global

“Last year demonstrated that it’s difficult to survive as an AI wrapper company,” George Mathew, MD at Insight Partners, told Crunchbase News in a trend piece published in January.

That sentiment has hardened into investor consensus through 2026. PitchBook analysts have documented investors nearly halting funding for horizontal, undifferentiated AI platforms, favouring companies with a proprietary data advantage, genuine compute economics, or workflow lock-in that a general-purpose chatbot cannot replicate overnight. The pitch-deck advice circulating among Silicon Valley accelerators this year is blunt: if a reviewer’s first reaction is “OpenAI wrapper,” the meeting is already over.

The mechanism is straightforward. Every capability a wrapper startup builds on top of GPT, Claude, or Gemini can, in principle, be absorbed into the next release of that same model. A startup whose entire product is a nicer interface to somebody else’s intelligence has no moat, only a head start, and head starts in this market now measure in months.

Southeast Asia’s version of the squeeze

The regional data bears this out with uncomfortable precision. Singapore alone accounts for roughly US$9.3 billion of Southeast Asia’s cumulative Native AI funding since 2019. On the other hand, Vietnam, Malaysia, Indonesia and Thailand have collectively raised less than US$40 million combined.

AI infrastructure (foundation models, compute platforms, and the picks-and-shovels layer) was the single most heavily funded segment in 2026, pulling in US$4.3 billion across 56 rounds, led by Kling AI and MiniMax’s US$1.2 billion round.

Also Read: Fintech, DeFi and applied AI define Southeast Asia’s new venture discipline

Investors are not walking away from Southeast Asian AI. They are walking straight past the application layer to write concentrated cheques into infrastructure and foundation-model plays domiciled almost entirely in one city-state.

That leaves a large, under-discussed population of genuinely useful but thinly differentiated GenAI tools — customer-service chat layers, document summarisers, marketing-copy generators built for SEA-specific languages and workflows — competing for a shrinking pool of smaller, earlier-stage cheques. Some of that work is legitimately valuable to the SMEs and enterprises using it. Very little of it, on current investor logic, is fundable as a stand-alone venture-backed company.

What actually separates a wrapper from a company

The startups clearing the bar globally share a pattern worth naming plainly, because it is achievable, not mystical. They own an exclusive dataset a general model cannot replicate, which is proprietary transaction, behavioural or domain data accumulated through actual usage. They have workflow lock-in deep enough that switching costs, not model quality, keep customers paying. And they can show a credible path to serving users profitably at scale, rather than assuming compute costs will simply keep falling in their favour.

For Southeast Asia specifically, that argues for leaning harder into precisely the terrain that is hardest for a Silicon Valley foundation model to serve well from the outside: hyper-local language data across Bahasa, Vietnamese, Thai and the region’s dozens of dialects; regulatory and compliance workflows tied to specific national frameworks; and vertical depth in sectors (logistics, agritech, healthcare compliance), where the value sits in proprietary operational data, not in the fluency of the underlying model.

The quiet exit ramp: consolidation, not collapse

It would be too simple to say wrapper startups simply fail. The more common outcome globally has been quiet absorption: talent acquihires, small tuck-in acquisitions by larger platforms wanting a distribution channel or a regional team, or founders folding a standalone product into a feature inside someone else’s suite.

Crunchbase News has tracked over 127,000 tech job cuts at US-based companies in 2025 alone, a chunk of it AI-adjacent restructuring rather than pure failure — talent being reallocated rather than simply let go.

Southeast Asia should expect the same pattern rather than a wave of dramatic shutdowns: wrapper startups that raised a seed round in 2024’s enthusiasm quietly becoming a feature at a larger fintech, super-app, or enterprise software company rather than an independent Series A story. That is a reasonable outcome for a founding team, but it is a very different one from the venture-scale exits the 2024 funding wave implicitly promised investors and early employees.

The uncomfortable question for founders and investors alike

None of this means Southeast Asia’s AI funding story is disappointing; US$4.1 billion is real capital, and infrastructure investment of this scale builds genuine regional capability over time. But founders currently raising on a “we built a nice interface to an LLM” pitch should treat 2026’s numbers as a warning rather than encouragement. The rounds are getting bigger for companies that have already proven defensibility, and smaller, or non-existent, for everyone else.

Also Read: Where AI money is made, and where SEA founders should actually compete

Investors, for their part, might ask themselves a harder question than “does this have a moat”: whether Southeast Asia’s own concentration of AI capital into Singapore-domiciled infrastructure plays is creating exactly the kind of regional imbalance the ecosystem has spent a decade trying to correct.

A funding boom that leaves Vietnam, Indonesia, Malaysia and Thailand collectively under US$40 million is not obviously healthier than the wrapper glut it is replacing; it is simply a different kind of concentration risk, one investors are currently far less inclined to name out loud.

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A great deal of ethical AI discussion still happens at a distance from the people who live with the system every day. It happens in governance forums, legal reviews, executive updates, risk committees, and product documents. All of that has value, but none of it answers the most revealing question.

When the system makes a bad call, can the person closest to the customer, patient, claimant, applicant, or case actually challenge it?

Contestability is the missing centre of ethical AI

Many organisations speak about fairness, accountability, transparency, and safety. Far fewer build properly for contestability. That matters because a system can be documented, monitored, and technically explainable, while still being very difficult to challenge in practice.

Contestability means more than having an override button buried somewhere in the workflow. It means the system is designed so that human disagreement is expected, legitimate, and operationally supported. It means people are not merely allowed to question an output in theory. They are able to do so without being punished by time pressure, managerial pressure, or the quiet cultural message that the machine is usually better.

This is the point many companies still miss. They think ethics is mainly about how the model behaves. It is also about how much room the organisation gives humans to resist the model when reality no longer fits the output.

Frontline employees often see harm before leadership does

One reason this question matters so much is that frontline employees are usually the first people to see where the system is breaking. They hear the confusion in a customer’s voice. They notice when a recommendation does not fit the case history. They see when a decision is technically consistent but practically absurd. They feel the human consequences before those consequences become a trend line in a monthly review.

Yet in many organisations, the frontline sits low in the hierarchy of trust. Their judgement is treated as anecdotal. Their objections are seen as local friction. Their escalations are sometimes tolerated, but not welcomed. The machine may be backed by data science, product, engineering, and leadership optimism, while the employee challenging it is backed only by experience and instinct.

Ethical AI becomes fragile when the people nearest to lived reality are expected to absorb the consequences of bad outputs without having the standing to challenge them. In those environments, the business still tells itself that humans remain in control. In practice, the human role has narrowed into delivery and damage management.

Also Read: AI agents could help Southeast Asian firms untangle cross-border payment costs

Override is not meaningful if it carries career risk

A lot of companies can point to formal override mechanisms. They will say the employee can escalate, pause the process, or route the case for review. On paper, that sounds reassuring. In reality, the value of override depends entirely on the surrounding culture.

Can a frontline employee challenge the model without being seen as inefficient?

Can they do it without creating a delay they will later be blamed for?

Can they do it without needing to prove the system wrong to a higher evidential standard than the system needed to make the recommendation in the first place?

Can they do it repeatedly if a pattern emerges, or only occasionally before they are labelled difficult?

Most organisations want the comfort of human judgement without the cost of supporting it

This is where the issue becomes uncomfortable in a useful way. Many firms want to claim that a human remains in the process, but they do not want to pay the operational price of making that human genuinely powerful.

Real challenge rights are expensive. They slow some decisions down. They require training. They require better case design, better escalation flows, and managers willing to back people who raise concerns. They require enough slack in the system for employees to think rather than merely process. They require leaders to accept that some machine recommendations will be questioned not because the model is broken, but because human reality is messy.

That is a much harder model than symbolic oversight.

So companies often settle for a compromise they do not describe clearly. The employee remains present, but not empowered. The organisation gets the reassurance of human involvement and the productivity profile of machine-led processing. The frontline becomes a moral buffer rather than a real decision maker.

Ethical AI depends on organisational, not just technical restraint

There is a tendency to frame ethical AI as a problem of model limits. Better testing, clearer thresholds, stronger policies, safer deployment. These all matter, but they do not solve the core institutional question.

Does the organisation have the courage to let people close to the work challenge the system, even when doing so slows things down, complicates reporting, or disrupts the story that the product is performing well?

Also Read: When AI starts thinking for us

That is the harder test because it touches power. It asks whether a call centre agent, claims reviewer, nurse, support specialist, operations analyst, or case worker can force the organisation to confront a failure before leadership is ready to admit it. It asks whether managers will protect that challenge or quietly discourage it. It asks whether product and engineering teams are willing to hear that a system which looks strong on aggregate is creating real harm at the edges where people live.

The best ethical systems treat disagreement as intelligence

One of the clearest signs of maturity is how a company interprets human disagreement with AI. Weak organisations treat disagreement as resistance. Strong ones treat it as intelligence.

When frontline employees contest outputs, they are often surfacing something the system cannot see properly. Missing context. Policy ambiguity. A rare case type. A hidden operational cost. A human signal that does not fit the data structure neatly. If the organisation is wise, it treats those moments as valuable evidence about the limits of the model and the design of the workflow.

That requires a change in posture. Instead of asking, “Why did the employee not trust the system?” leaders should also ask, “What did the employee see that our system or process could not absorb?”

That is a much more serious question. It turns challenge into a source of institutional learning rather than a local inconvenience.

The frontline is where ethical claims become real

Every company can produce a responsible AI statement. Every company can describe review processes and approval structures. But the real meaning of those claims is tested in much simpler moments.

• A customer says the decision makes no sense.
• A patient’s situation does not fit the score.
• A claimant has evidence the workflow did not capture.
• A support case carries a kind of vulnerability the model was never trained to interpret well.
• A fraud flag looks statistically plausible but humanly wrong.

In those moments, ethics is no longer a principle. It becomes a live question of whether the employee in front of the issue can act on their judgement and be backed when they do.

That is why frontline challenge rights matter so much. They are where all the lofty language either survives contact with reality or quietly collapses.

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Ask AI how to improve a factory, a clinic, a logistics company, or a retail business, and it will have plenty to say.

It can list ideas, explain trends, draft plans, compare options, and make a rough proposal sound persuasive. In a few minutes, it can produce the kind of first draft that once took a junior team days.

That is useful.

It is also easy to mistake useful information for good advice.

AI can suggest 20 ways to improve a factory. The person who has spent 10 years on that factory floor may know which 19 will fail by Friday.

That is not because the experienced person knows more facts. AI may have more facts than either of you can read in a lifetime.

It is because experience gives people judgment.

Answers are becoming cheap

For a long time, access to information was an advantage. If you knew where to look, which expert to call, or how to write a decent first draft, you could move faster than someone who did not.

AI is lowering that advantage quickly.

Research summaries, product ideas, basic code, marketing copy, customer emails, and business plans are becoming faster and cheaper to create. The cost of a first attempt is approaching zero.

Also Read: The transformation ecology crisis: How AI is exposing the hidden fragility of high-performing teams

This is good news for people starting with less money or fewer connections. A graduate can explore an idea without hiring a consultant. A small business can analyse feedback without a large research team. A retrenched manager can turn years of experience into a clearer plan for a new service or product.

But when everyone can get an answer, the value shifts to deciding which answer matters.

Experience is a filter

Domain expertise is not a collection of facts stored in someone’s head. It is a filter built through repetition.

It tells a nurse which symptom matters first. It tells a procurement manager which supplier promise will collapse under pressure. It tells a mechanic which sound is serious. It tells a founder which customer complaint is a real market signal and which one is simply a loud opinion.

This kind of knowledge is often hard to explain because it is practical. It is built from bad decisions, difficult customers, failed projects, missed deadlines, and work that went wrong when the presentation said it should go right.

AI can give an expert more options. It can help them see patterns, organise information, and test ideas. But it cannot know the local constraints unless someone who understands them provides the context.

That makes experienced people more important, not less.

The best prompts come from people who know the work

A weak question gets a weak answer.

Ask AI, “How can I improve my logistics business?” and you will receive a polished set of general suggestions. Ask, “How can I reduce failed same-day deliveries in Jakarta during peak rain, without adding drivers or breaking our margin?” and the answer becomes more useful.

The difference is not the tool. It is the person asking.

Domain experts know what to include in the question. They know which limits cannot be ignored. They know the customer, the workflow, the budget, the regulation, and the inconvenient detail that changes everything.

Also Read: Why Malaysia’s AI Nation 2030 plan matters for B2B startups

That is why a person with real experience can use AI as leverage. They can turn a broad suggestion into a practical test. They can spot an idea that will not survive the real world. They can notice a useful improvement that someone outside the industry would never have seen.

Creation may be cheap, judgment is not

As the cost of producing a first draft falls, creating something average will become easier.

The valuable work will be deciding what is worth creating at all.

This matters for graduates. The goal is not only to learn how to use AI. It is to get close to real work, real customers, and real consequences. Those experiences create the judgment that makes AI useful.

It also matters for workers who are retraining after a career change. Years spent in an industry are not obsolete because a chatbot can explain the industry. Practical knowledge is often the best starting point for a service, product improvement, or invention.

And it matters for SMEs. Their most valuable knowledge may sit with the people who talk to customers, run the machines, manage the suppliers, and fix problems when nobody else knows what to do.

If that knowledge leaves with an employee, it may leave without being captured. If it is noticed, documented, and developed, it may become a better product, a trade secret, a patentable invention, or a stronger way of working.

AI helps expertise travel further

The hopeful story is not that AI replaces the expert. It is that AI can help the expert do more.

A food producer can explore ways to reduce waste. A technician can turn a recurring repair into a design improvement. A logistics manager can use operational data to test a better route or handover process. A clinician can identify a problem in a care journey and explore a safer solution.

The tool reduces the cost of exploration. The expert supplies the judgment.

That combination is powerful because it lets practical experience travel further. It can move from a private insight to a documented process, a tested product, a protected invention, or a business that serves more people.

The future will reward people who know how to use AI. But it will reward even more those who know what AI should be used for.

AI has answers.

Experience knows which ones matter.

If you have practical knowledge that could become a product, process, or invention, start brainstorming it at IPGuru.ai.

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