Acrab Inc., a Singapore-headquartered frontier tech company, has announced it has secured more than US$350 million in cumulative financing since its founding in 2024, as it looks to establish itself as a foundational infrastructure provider for the next generation of agentic AI.

The company, which had previously operated in stealth, said the financing was raised across multiple rounds and includes participation from global venture capital firms and strategic industry investors. Vertex, the global venture platform backed by Singapore state investor Temasek, was among the earliest backers through its Vertex Ventures SEA & India and Vertex Growth funds, and has continued to increase its commitment as Acrab reached key technological milestones.

The announcement also marks the commercial debut of GΞLIX, Acrab’s first-generation compute platform designed to support local LLM inference and agentic AI workloads at the edge. The company said GΞLIX has been validated in demanding real-world deployment environments and is progressing toward first industry adoption and mass production.

Acrab’s full-stack architecture spans AI silicon, local LLM inference, operating systems, multi-modal human-machine interface (HMI), and agent orchestration technologies. The company positions its approach around heterogeneous computing — specifically, the coordinated interaction between central processing units (CPUs) and neural processing units (NPUs) — which it describes as central to the performance demands of agentic AI systems.

Also Read: Top 4 Best ERP for Large Enterprise in Malaysia

“CPUs are becoming increasingly important as AI systems evolve into heterogeneous computing environments, where execution depends not only on NPU performance, but on the seamless coordination between CPUs and NPUs,” said Dr. Ken Phua, CEO of Acrab. “Delivering this new generation of agentic experiences calls for a fundamentally new compute foundation.”

Phua brings extensive experience from the semiconductor sector, including a career at Arm UK, where he led Asia Applications Engineering and co-led global IP strategies, before serving as co-CEO of Arm China.

Vertex’s Kee Lock Chua, CEO of Vertex Holdings, said the firm’s conviction in Acrab rests on a belief that the next wave of AI will run at the edge rather than in the cloud. “Our confidence has only deepened as the team has translated that thesis into a validated platform, and we’ve increased our commitment at every step,” he said.

Acrab’s target markets span personal AI PCs, home hub devices, in-vehicle intelligence, industrial operations, and general robotics — environments the company believes will increasingly require private-by-design, context-aware computing that can carry out tasks in real time without relying on cloud infrastructure.

Also Read: Vietnam isn’t just inviting private capital in. It is structurally dependent on it

The broader thesis underpinning Acrab’s strategy is a distinction between generative AI, which responds to prompts, and agentic AI, which the company characterises as systems capable of inferring intent, co-ordinating tools, and executing tasks on behalf of users — a shift that demands a new class of compute infrastructure.

Acrab said it will use the new capital to accelerate platform development, expand partnerships with global technology firms, and strengthen its presence in key international markets.

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Carro, the Singapore-headquartered online automotive marketplace that has built a significant presence across Southeast Asia, has made its first major move into Australia with the acquisition of CarPlace, a used-car platform operated by Australian automotive group Autoleague.

The deal gives Carro a physical presence in three of Australia’s four largest markets in one go. It signals a strategic effort to export its technology-rich approach beyond Southeast Asia. For an industry accustomed to fragmented dealer networks and opaque pricing, Carro is pitching transparency, end-to-end workflows and a stronger wholesale pipeline, including vehicles from Japan, as its differentiators.

Also Read: Profitable, AI-driven, and IPO-ready: Inside Carro’s next chapter

A foothold Down Under

CarPlace, positioned among Australia’s more prominent used-car platforms, will remain linked to Autoleague, which becomes a strategic shareholder in the combined business. Autoleague’s continued involvement provides Carro with an immediate network of dealerships and operational scale that would take years to build organically.

Aaron Tan, Carro’s co-founder and chief executive, framed the move in blunt commercial terms: Australia, he said, is “one of the largest used car markets in Asia Pacific, with a consistent annual sales volume of 2.3 million used cars and fast-growing EV penetration.” He argued the market is “ripe for a platform like Carro to transform the used car landscape and deliver a better, convenient, more transparent customer experience that is powered by technology and AI.”

Tan’s pitch rests on a two-pronged strategy:

1. Strengthen retail pre-owned operations using Carro’s proprietary tools, such as vehicle inspections, inventory tracking, asset and lead management.
2. Grow wholesale activity, leveraging Carro’s Japan presence, to serve the Japan–Australia import corridor.

The latter is notable because Japanese models remain popular in Australia, and a reliable cross-border wholesale channel could be a lucrative niche.

What Carro brings: technology and dealer relationships

Carro’s operating model in Southeast Asia blends marketplace listings with back‑end services for dealers, combined with data and AI to reduce information asymmetry. In its markets, Carro has emphasised standardised inspections and clearer pricing, both of which can shorten transaction times and reduce post-sale disputes.

Also Read: Carro acquires Beyond Cars, bets big on Hong Kong’s strong EV growth

“Building and maintaining a strong dealer network has always been key for all the markets we’re in,” Tan said. “Carro has a track record of partnering with local dealers to support their growth, and we welcome ‘win-win’ partnerships in Australia. We’re confident in our strong Wholesales capabilities, thanks to our presence in Japan.”

For Australian dealers, access to Carro’s inspection protocols, lifecycle management tools, and potential new supply from Japan may help both sourcing and remarketing. For consumers, the promise is greater transparency on vehicle condition and pricing, a recurring sore point in used-car markets globally.

Autoleague’s backing reduces execution risk. Dan Kawai, Autoleague’s managing director and CEO, welcomed the partnership: “We’ve seen Carro’s technology infrastructure, streamlined operations, and unwavering commitment to transparency within the industry, and we’re confident in their goal to become a leading player in Australia.”

Southeast Asian angle and regional implications

Carro’s expansion is relevant to Southeast Asia’s increasingly competitive used-vehicle ecosystem. Over the last decade, regional players have experimented with a range of models: pure marketplaces, trade-only exchanges, and vertically integrated services that include financing, repairs and logistics. Carro’s Australia move demonstrates a reverse flow of scale: a Southeast Asian-grown company exporting its playbook to a developed market.

That matters for several reasons. First, it underscores the maturing capabilities of Southeast Asian tech startups, many of which have moved beyond consumer-facing apps into complex logistics and asset-heavy categories. Second, if Carro successfully integrates Australian dealers and the Japanese supply chain, it could create a blueprint for other regional expansions, including more formalised import/export routes between Japan, Southeast Asia, and Australia.

Finally, the move could stoke competition in Australia from both local incumbents and other regional challengers. Sellers and dealers in Southeast Asia may face new competition for Japanese used-vehicle stock if Carro scales its wholesale operations across borders.

Challenges ahead

Carro’s promise of technology-driven clarity is persuasive on paper, but execution risks are real. Australia’s market is dispersed and regulated at the state level for vehicle inspections, registrations, and consumer protections. Winning consumer trust will require consistent inspection standards and reliable post-sale support, areas in which established local players have credibility.

Moreover, scaling a Japan-Australia import corridor means handling customs, compliance, logistics, and model homologation issues, tasks that can be capital-intensive and operationally complex. The economic case depends on margins after shipping and compliance costs, especially for lower-priced models.

Also Read: Carro invests in digital content, marketing services agency Driven Communications

There is also the question of brand recognition. While Carro is well known in Southeast Asia, Australian consumers and dealers may initially view the company as an outsider. Close cooperation with Autoleague and local dealers could mitigate that, but it will take time to convert partnerships into sustained market share.

What to watch next

In the near term, Carro will likely focus on plugging CarPlace’s dealer network into its tech stack, standardising inspections, and piloting Japan-sourced inventory flows. Observers should monitor three metrics: dealer uptake of Carro’s platform features, wholesale volumes moving from Japan to Australia, and post-purchase consumer satisfaction scores.

If Carro succeeds, the acquisition will mark a pivotal moment for Southeast Asian mobility tech firms: proof that their operational models can scale into developed markets and that regional cross-border automotive supply chains are commercially viable.

For Australia’s used-car market, the arrival of a technology-led, cross-border operator could accelerate transparency and digitisation to the benefit of buyers and dealers alike, but only if the promises match the execution. The partnership with Autoleague gives Carro a bridge; the rest will depend on how quickly it turns that bridge into reliable, everyday commerce.

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A Jakarta courtroom delivered a verdict on Thursday that is already reverberating far beyond Indonesia’s borders.

Four senior executives from two of the country’s most prominent state-backed venture capital firms, MDI Ventures and BRI Ventures, have been sentenced to prison over investments made in the now-defunct agritech startup TaniHub.

Also Read: Indonesia detains 3 more suspects in TaniHub investment fraud case

The ruling has sent a chill through the regional startup ecosystem, prompting an immediate and forceful pushback from the defence and igniting a broader debate about the criminalisation of business failure.

The sentences

The Jakarta Corruption Court sentenced former MDI Ventures CEO Donald Wihardja to five years in prison and fined him US$28,000, with a substitute sentence of six months in detention should the fine go unpaid. His former vice president of investment, Aldi Adrian Hartanto, received a two-year prison term and a US$14,000 fine.

On the BRI Ventures side, former CEO Nicko Widjaja was handed a three-year sentence and fined US$19,600, with 110 days of additional imprisonment as a final substitute if asset seizure proves insufficient. Former vice president of investment William Gozali received two years and a US$14,000 fine.

Prosecutors alleged that all four approved investments in TaniHub without performing adequate due diligence, leaning too heavily on data supplied by the startup itself. Authorities put MDI Ventures’s losses to the state at approximately US$20 million and BRI’s at around US$5 million.

How a farming unicorn hopeful unravelled

Founded in 2016, TaniHub was once among Indonesia’s most celebrated agritech bets. The company raised US$92.5 million in total disclosed funding over its lifetime, including a high-profile US$65.5 million round in 2021 in which both MDI Ventures and BRI Ventures participated alongside other investors. It positioned itself as a transformative platform connecting smallholder farmers to buyers and, eventually, consumers, scritping compelling story in a country where agriculture employs nearly a third of the workforce.

The story, however, did not end well. TaniHub ran into severe financial difficulties, carried out sweeping layoffs, and eventually wound down much of its operations. By September 2023, the value of TaniHub shares held by BRI Ventures had fallen to approximately US$380, a near-total write-off on what had been a US$5 million position.

Also Read: “Special Projects” and shady metrics: TaniHub whistleblower speaks as top execs detained

It was that collapse in value that prosecutors and, ultimately, the court treated as evidence of state financial loss.

What the court found

In the case against Widjaja, the Panel of Judges found that BRI Ventures’s investment process, split between a US$2 million Series A+ round and a US$3 million convertible note round, violated the prudential principle on multiple counts.

The court found that the deep feasibility study relied too heavily on TaniHub-supplied data, without sufficient independent verification, and that the analysis was based in part on unaudited financial statements. The outstanding receivable concerns were not adequately interrogated.

The panel also noted that the investment committee at the time consisted only of the president director, and that the oversight function of the board of commissioners had not operated optimally.

On the question of personal enrichment, a standard element in Indonesian corruption cases, the court acknowledged that Widjaja received no personal benefit whatsoever. Nevertheless, it held the element fulfilled because the US$5 million flowed to TaniHub as a third-party corporation. The court further held that cooperation in the investment decision-making process was sufficient to establish joint participation without requiring proof of an explicit agreement.

The defence fires back

Widjaja’s legal team, led by Ditho Sitompoel, did not mince words in their response.

“The line between a failed investment decision and a criminal act must be carefully preserved, so that criminal law is not used to judge a business decision based solely on its outcome — that is, hindsight bias,” Sitompoel said in a statement.

The defence raised six pointed objections to the court’s reasoning. Chief among them was the argument that the panel’s prudential standard was simply wrong for the asset class. Venture capital, by its very nature and by the explicit mandate of POJK 35/2015 (Indonesia’s regulatory framework for the industry), requires firms to take measured risk on high-growth companies that are often pre-revenue, unaudited, and operating at a loss. Holding a VC firm to the verification standards of a bank extending credit to a mature business, the defence argued, would render nearly every venture investment in Indonesia legally suspect.

The team also highlighted that BRI Ventures had, upon identifying deteriorating conditions at TaniHub, halted Series B funding and pursued divestment, hardly the conduct of a reckless actor. The panel acknowledged these steps but held that they did not negate the original unlawful act.

Perhaps most significantly, Sitompoel pointed to the systemic implications. “If investment failures that went through proper approval, review, and governance can still be criminalised, this creates legal uncertainty with the potential to produce a chilling effect on investors, directors, commissioners, and professionals who are required every day to take the legitimate business risks needed to drive economic growth.”

A verdict with consequences beyond one courtroom

The concern is not abstract. Indonesia has spent years trying to build itself into a credible destination for venture capital, and state-backed funds like MDI Ventures and BRI Ventures have been central to that ambition, deploying government-linked capital into the startup ecosystem at a time when private capital was still finding its footing.

Also Read: Nicko Widjaja’s legal defence team on the prospect of winning: “We are confident enough”

The TaniHub verdict raises an uncomfortable question: if a VC fund manager at a state-linked institution can be imprisoned when a portfolio company fails, even absent any personal gain or proven misconduct, who in their right mind will take the job?

The four convicted executives are widely expected to appeal. How Indonesia’s higher courts handle those appeals may well determine the country’s investment climate for years to come.

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Banks are still talking about AI governance as though it belongs mainly to policy teams, future regulation, and committee oversight. That view is already out of date. AI governance is now an operating model issue.

The real danger is not only that a model makes a poor recommendation or that an employee shares sensitive information with the wrong tool. Those risks matter, but they are also the easiest to recognise. The deeper problem is that AI adoption is spreading faster than many banks can govern judgment, accountability, customer treatment, and operational response.

That matters because banking is not judged only by the quality of its policies. It is judged by whether decisions can be explained, defended, and corrected when customers are affected. If AI use spreads faster than the control model around it, the consequences will not stay inside technology teams. They will surface in complaint handling, operations, fraud casework, collections, service recovery, and exceptions, which are all areas where judgment carries regulatory and reputational weight.

The wrong starting point

A great deal of discussion still begins with model risk, privacy, or compliance. These issues matter, but they are not the most useful place to begin. A better question is this: what happens to banking operations when employee judgement is partly assisted by systems that are fast, persuasive, difficult to challenge, and unevenly understood across the organisation?

That question changes the conversation. It moves the bank away from abstract talk about AI innovation and towards the daily reality of operational decisioning. It forces leaders to look at how a complaints handler uses AI to summarise a case, how an operations analyst drafts an exception rationale, how a collections agent shapes a customer conversation, or how a fraud investigator relies on AI driven prioritisation.

In each case, the issue is not simply whether the tool exists. The issue is whether the bank can still stand behind the quality, fairness, and traceability of the resulting action. That is the point where AI governance stops being a technology topic and becomes a banking discipline.

Banks are governing two different problems

One reason many governance frameworks remain vague is that banks often blend together two different forms of AI use.

The first is employee AI use. This includes drafting, summarising, searching, translating, analysing, and preparing work. On the surface, that can look like a productivity issue. In practice, it is also a judgment and control issue. Once employees rely on AI to shape internal outputs, customer responses, escalation summaries, or operational recommendations, the work is no longer purely human in the old sense. It becomes a blended output. That means the bank needs clear rules on what can be delegated, what must be checked, and what remains fully owned by the employee.

Also Read: The shadow ledger: Why AI governance is the new architecture of brand trust and enterprise revenue

The second is AI-assisted business decisioning. This sits much closer to customer outcomes, prioritisation, approvals, exceptions, operational triage, and vulnerability handling. Here, the stakes are higher because AI is not only improving speed or language. It is influencing actions that can shape fairness, access, treatment, and trust.

These two categories require different controls. Too many governance models treat them as one broad AI issue and end up being both too loose and too blunt.

The first failures will be quiet

Banks often expect AI risk to arrive as a major breach, a headline incident, or an obvious model failure. That expectation is misleading. The first operational failures are more likely to be quiet and cumulative.

Sensitive data starts appearing in prompts because employees are under pressure, and approved workflows are slower. AI-generated summaries begin to flatten nuance in complaint files, which weakens later review. Decision support outputs become trusted because they sound coherent, even when the reasoning beneath them is thin. Frontline teams rely on wording that is technically acceptable but badly judged for vulnerable customer situations. Managers approve outputs without really knowing how much human challenge has been applied. Control teams discover that the audit trail feels sufficient for internal reassurance but is not strong enough for real scrutiny.

None of these issues looks catastrophic in isolation. Together, they create a more serious pattern. The bank begins to lose clarity about where human judgment ends, and AI-shaped judgment begins.

Why traditional model governance is not enough

Banks already know how to govern models in a traditional sense. They have approval forums, validation standards, documentation requirements, monitoring routines, and control owners. Those disciplines still matter, but they are not sufficient for the current AI wave.

What is needed now is governance of assisted judgment.

That means understanding not only whether a model is technically sound, but also how AI-shaped outputs enter workflows, how much challenge is realistically applied by employees, where reliance becomes routine, how explanations are captured, how errors are detected, and how customer harm is identified when the decision was not fully automated yet was no longer fully human either.

Also Read: Why emerging markets need AI governance infrastructure before AI scale

This is where current governance language can become misleading. Many organisations take comfort from calling a tool assistive rather than determinative. In practice, assistive systems can heavily shape outcomes because they influence what employees see first, how they frame the case, what options appear reasonable, and how quickly they move.

Complaint handling is the real test

Many banks will discover the quality of their AI governance not through a model review, but through complaints.

Complaint handling is where customer harm, operational judgement, fairness, explanation, and record quality all come together. If AI is being used poorly elsewhere in the operation, complaint handling is often where the weaknesses become visible. Cases become harder to assess. Summaries lose context. Root cause becomes harder to establish. Responses sound polished but unconvincing. Reviewers struggle to tell whether the original decision reflected genuine consideration of the customer’s circumstances or simply repeated an AI-shaped conclusion.

This is why complaints should be treated as a primary governance lens. If a bank cannot defend AI-influenced operational decisions inside its complaints process, it is not governing them properly. If complaint handlers do not know where AI was used earlier in the customer journey, they cannot properly assess fairness. If complaint responses themselves are AI-assisted, the bank needs very clear boundaries on what can and cannot be delegated.

A better model

A stronger governance model starts with use case classification. Banks need to separate low-consequence productivity support from high-consequence decision support, while also recognising the large middle ground where AI is not making the final decision but is materially shaping human judgment.

From there, governance needs to move into workflow design. Where is AI allowed? What data can be used? What outputs are acceptable? What level of verification is required? What must be recorded? What escalation is needed if the AI output conflicts with case evidence or customer context?

After that comes monitoring. Not only whether a tool is secure and available, but how it is actually being used, where teams are becoming over-reliant, where outcomes are drifting, and which workflows are creating defensibility problems.

Then comes response capability. If something goes wrong, the bank needs to know how to pause usage, investigate the impact, identify affected customers, explain what happened, and remediate quickly. Governance without response capability is only paperwork.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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Most voice AI demos are built for perfect conditions.

The room is quiet. The Wi-Fi is stable. The user speaks clearly, waits for the response, and follows the script. In that setting, many modern voice agents can sound impressive.

The real test begins outside the demo room.

A user in Jakarta speaks over mobile data during a commute. A child in Manila interrupts an AI tutor halfway through an explanation. A customer in Ho Chi Minh City switches between English and Vietnamese. A smart device in Thailand moves from stable Wi-Fi to weak 4G.

Suddenly, the AI no longer feels intelligent. It feels delayed, stiff, and difficult to trust.

For teams building voice AI in Southeast Asia, this is the uncomfortable lesson: the issue is not always intelligence. It is whether the full voice experience can survive real-world conditions.

Physical AI needs more than a voice

Physical AI is not just AI inside a device.

It is AI that lives in the user’s environment and becomes part of daily life. It may appear as an educational toy, a wearable translator, a companion robot, an in-car assistant, a smart camera, or a home device that can talk, listen, and respond.

That changes the standard for user experience.

When people type into a chatbot, waiting feels normal. The interaction is already asynchronous. A short delay may be acceptable because the user is looking at a screen and expecting software to process a request.

When people speak to a device in the room, the expectation is different. They expect rhythm. They expect responsiveness. They expect the device to understand when a conversation has started, shifted, or ended.

This is why physical AI has to be evaluated differently from text-based AI. It is not enough for the answer to be correct. The interaction has to feel natural in the moment.

The bot feeling usually starts with latency

When users say a voice AI “feels like a bot,” they are often reacting to timing before they are reacting to content.

A pause after every sentence makes the experience feel mechanical. A delayed answer makes the user wonder whether the device heard them. A voice agent that continues speaking after the user has moved on feels disconnected from the conversation.

This sensitivity is not new. In real-time voice traffic, Cisco cites the ITU G.114 recommendation of less than 150 milliseconds of one-way end-to-end delay for high-quality voice. Voice AI adds more layers on top of that, including speech recognition, model response, speech output, and routing between services.

Also Read: To Voice AI or not – The changing face of customer experience

Human conversation depends on small timing cues. We pause, overlap, correct ourselves, interrupt politely or impatiently, and change direction mid-thought. These are not edge cases. They are the normal shape of how people speak.

Voice AI breaks when it treats conversation as a clean sequence: user speaks, machine processes, machine replies.

A more natural system needs a fluid loop. It has to listen, process, generate, and speak with minimal delay. It also has to adapt when the user changes direction. That requires real-time audio transport, streaming speech recognition, response generation, speech output, and interruption handling to work together.

For builders, latency is not just an engineering metric. It is part of the product’s personality.

Southeast Asia turns weakness into a market problem

Southeast Asia is an important region for voice-first AI because the use cases are practical.

The region’s digital economy is already large enough for these experiences to matter at scale. Google, Temasek, and Bain estimate that Southeast Asia’s digital economy is set to surpass US$300 billion in gross merchandise value by 2025. The same report frames the region’s next phase around AI adoption, after years of growth in digital services.

The opportunity is clear: mobile-first users, multilingual households, growing demand for education technology, rising adoption of connected devices, and many situations where voice can make technology easier to use. A screenless device, a voice tutor, or a smart assistant can be valuable when typing is inconvenient, when users move between languages, or when a product is used by children, older adults, or workers on the move.

But the same conditions also make the region difficult.

Indonesia shows the scale and complexity. DataReportal counted 212 million internet users in Indonesia at the start of 2025, along with 356 million cellular mobile connections, equivalent to 125 per cent of the population. Yet Ookla data cited by DataReportal showed a median mobile internet download speed of 29.06 Mbps. For voice AI, that gap between massive connectivity and uneven consistency is where user experience problems appear.

The fragmentation is regional, not just local. Data found that mobile-only smartphone users make up less than 10 per cent of users in markets such as Vietnam, Brunei, and the Philippines, but more than a quarter in East Timor, Laos, Thailand, and Malaysia. In Laos, Thailand, Malaysia, Cambodia, and Indonesia, more than 40 per cent of smartphone users have no or very limited Wi-Fi use.

This turns voice quality from a technical detail into a market expansion issue.

If a product only works in controlled conditions, it cannot scale confidently across the region. If it struggles with accents, unstable networks, or mixed-language behaviour, users will not wait for it to improve. They will stop using it.

Also Read: How voice AI is revolutionising the fintech scene

The stack needs to be built for interruption

A strong physical AI product needs more than a model and a synthetic voice.

• The device needs reliable audio capture: If the microphone hears too much background noise or misses the wake word, the experience fails before the model is involved.

• The voice pipeline needs low-latency transport: Audio has to move quickly between the device, the cloud, and the AI services without adding noticeable delay.

• The system needs interruption handling: Humans do not wait politely for a machine to finish talking. They correct it, interrupt it, and change direction. A natural voice agent must be able to stop, listen, and respond without making the user repeat everything.

• The AI needs memory and context: This is where physical AI starts to feel different from a basic voice bot. A companion device that remembers preferences, routines, or past interactions can create a sense of continuity.

• The product needs a persona: Not every device should sound friendly. Some should sound calm, professional, playful, or neutral. A toy, a healthcare assistant, and an in-car agent should not share the same personality.

The “soul” of physical AI comes from this full stack. The model matters, but it is only one part of the experience.

Builders should measure the conversation, not just the model

Many teams still evaluate voice AI by asking which model is the smartest.

That is too narrow.

A better question is: what does the full conversation feel like in the user’s actual environment?

For teams building in Southeast Asia, that means testing on mobile data, not just office Wi-Fi. It means testing noisy rooms, not just quiet meeting spaces. It means testing repeated use, mixed-language behaviour, unstable networks, and users who do not follow a script.

Product and procurement teams should ask practical questions before committing to a voice AI stack:

• What does the experience feel like on a real 4G connection?
• How quickly can the agent respond during natural turn-taking?
• Can it handle a user changing direction mid-conversation?
• What happens when the network becomes unstable?
• Can the stack support different models, speech providers, and deployment needs?
• Can the product preserve useful context across sessions?
• Can the voice persona be adapted for different markets and product categories?

Also Read: Never fear, AI is here: Helping midlife artists build their social media voice

The next AI device will be judged by how it feels

The next wave of AI will not stay inside chat windows.

It will live in toys, robots, wearables, cars, cameras, appliances, and industrial devices. In those environments, users will judge AI less like software and more like something sharing their space.

They will notice whether it listens at the right moment. They will notice whether it talks too much. They will notice whether it remembers. They will notice whether it is helpful or frustrating when the environment becomes noisy and unpredictable.

For Southeast Asia, this is both a challenge and an advantage. The region is difficult to build for because of its network complexity, language diversity, and mobile-first behaviour. But any physical AI product that performs well here will be stronger in many other markets.

The question for builders is no longer whether voice AI can speak. The question is whether it can stay useful when the real world gets messy.

If your physical AI device had to hold a natural conversation today on a crowded Southeast Asian mobile network, would it still feel alive?

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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In a year when artificial intelligence (AI) dominated the global investment conversation and every fund manager worth their carried interest was racing to stake out positions in deeptech, Vietnam’s private equity market made its biggest sectoral bet on something considerably more prosaic: food, beverages, and everyday consumer goods.

According to the Vietnam Innovation and Private Capital Report by DO Ventures and Boston Consulting Group, consumer staples attracted US$1.2 billion in private equity investment in 2025, the highest single-sector allocation in a decade. In a year when total PE deployment hit a record of US$3.96 billion, consumer staples alone accounted for roughly 30 per cent of the entire market. This was not a rounding error or a one-off anomaly. It was a deliberate, large-scale conviction bet by some of the most sophisticated capital allocators operating in Southeast Asia.

Also Read: Vietnam’s AI funding just grew 13x in two years. Now comes the hard part

The question is why and what it reveals about how serious investors actually think about Vietnam’s growth story beneath the tech-forward surface narrative.

A 100M-person consumption engine

Vietnam’s population crossed 100 million in 2023, making it the third most populous country in Southeast Asia after Indonesia and the Philippines. But the more important numbers are not the headcount, but the demographic composition and the income trajectory behind it. Vietnam has one of the youngest median ages in the region, a rapidly expanding urban middle class, and a sustained multi-decade record of GDP growth that has consistently outpaced regional peers. Per capita income has more than doubled over the past decade and is projected to continue rising sharply through the end of the decade.

For consumer goods companies, this combination of large and growing population, rising incomes, accelerating urbanisation, and shifting consumption habits is precisely the macroeconomic environment that generates durable, compounding revenue growth. The thesis is not complicated: as Vietnamese households earn more, they spend more, trade up to branded products, shift from wet markets to modern retail, and increasingly purchase food and beverage products with margins capable of supporting institutional investment at scale.

This is a story that global consumer PE firms know well, having played it out in China, India, and Indonesia over the previous two decades. In each of those markets, the early movers who invested in consumer staples during the inflexion point of middle-class formation generated outsized returns. Vietnam is at or near that inflexion point now.

Why PE, and why now

Private equity is structurally well-suited to the Vietnamese consumer opportunity in ways that venture capital is not. VC is designed for high-risk, high-uncertainty bets on business models that may not have proven themselves commercially. Consumer staples companies, such as established brands, predictable revenue streams, and physical distribution networks, are precisely the kind of assets that PE investors can underwrite with conventional financial analysis, apply operational improvement playbooks to, and exit at a premium through strategic sales to multinationals or domestic conglomerates hungry for bolt-on acquisitions.

The timing of the 2025 surge also reflects a specific market dynamic. Vietnam’s consumer sector has been consolidating steadily, with stronger brands gaining share at the expense of fragmented, subscale competitors. PE investors are positioning to back consolidators, including companies with distribution reach, brand equity, and operational efficiency to absorb market share and, ultimately, serve as acquisition targets for global fast-moving consumer goods giants looking to establish or deepen their Vietnam footprint.

Also Read: 48 PE investors, US$3.96B deployed, and not a single IPO exit in five years. Something is broken.

The doubling of active PE investors to 48 in 2025 has also intensified competition for quality assets, which tends to concentrate capital into the most defensible sectors. Consumer staples, with their resilient cash flows and relatively transparent valuation frameworks, offer a degree of predictability that is scarce in the current environment.

The modern retail inflexion

One structural shift accelerating the consumer staples investment thesis is the rapid modernisation of Vietnam’s retail infrastructure. Traditional trade — wet markets, small independent shops, informal distribution — still accounts for the majority of consumer goods sales in Vietnam, but modern trade is growing fast. Supermarket chains, convenience store networks, and e-commerce platforms are expanding aggressively, and their growth is directly beneficial to branded consumer goods companies that have the product quality and marketing capability to compete on modern retail shelves.

E-commerce, in particular, is reshaping the distribution economics of the sector. Platforms including Shopee, Lazada, and TikTok Shop have given consumer brands direct access to a nationwide customer base without the capital expenditure required to build physical distribution.

For PE-backed consumer companies, this is a margin and velocity story: the ability to reach consumers more efficiently, gather data on purchasing behaviour, and iterate product offerings creates compounding competitive advantages that drive valuation uplift over a typical five-to-seven-year investment horizon.

The risks that the headline number obscures

The US$1.2 billion figure is impressive, but it arrives with caveats. Consumer staples investments in Vietnam are not immune to macro headwinds that affect every asset class. Global commodity price volatility, particularly in agricultural inputs, packaging materials, and energy, can compress margins rapidly in food and beverage businesses. Vietnam’s export-oriented manufacturing sector, which underpins much of the consumer income growth story, is exposed to trade policy shifts and global demand cycles that are difficult to forecast.

There is also a valuation question. The intensity of PE competition for consumer assets in 2025 raises the possibility that entry multiples have stretched beyond what underlying fundamentals justify. If the exit environment remains constrained and the report’s own data on the absence of VC and PE-backed IPOs over the past five years suggests it does, then even well-performing consumer businesses may find it difficult to generate the exit returns that justify aggressive entry pricing.

None of this invalidates the underlying thesis. Vietnam’s consumer story is real, durable, and supported by demographic forces that do not reverse on a quarterly earnings cycle. But the smartest investors in the room are not just buying the macro narrative; they are underwriting specific companies with specific competitive positions, and the quality of those underwriting decisions will determine whether 2025’s record consumer staples allocation looks prescient or premature in five years.

Also Read: From frontier to emerging: How Vietnam’s stock market rewrote the ASEAN playbook in 2025

What is clear is that the most sophisticated private capital in Vietnam is not waiting for the country to become something it isn’t. It is betting heavily on what Vietnam already is: a massive, fast-growing domestic market with an appetite for better products. Sometimes the most contrarian trade is the most obvious one.

The post Vietnam’s biggest PE bet of 2025 was not on tech. It was on what 100M people eat every day appeared first on e27.

A lithium battery explosion in a Singapore residential garage is not the kind of event that typically sparks a deeptech startup. But for Muriel Demarcus, a seasoned infrastructure risk professional with three decades of managing billion-dollar projects across Europe and Asia Pacific, it was the moment everything clicked.

“My neighbour’s garage burned to the ground,” she recalls. “A lithium battery exploded. Nobody was hurt, but it was a close call, and it stopped me in my tracks. I had spent thirty years managing billion-dollar infrastructure risks. And here was a failure mode sitting in a residential garage that no system had caught, because no system was looking.”

Also Read: The AI upskilling wave is real but the gap it leaves behind is growing

That singular moment of frustration led Demarcus to upskill in AI in Singapore, revisit a question she had been asking in control rooms for decades, and eventually found Marsham Edge, a platform built around a deceptively simple but deeply difficult premise: in high-stakes environments, an alert you cannot explain is an alert you cannot act on.

Three agents, one mission

At the heart of Marsham Edge are three AI agents Argo, Ken, and Deb — each of which owns a distinct layer of the detection pipeline.

Argo manages data ingestion, validation, deduplication, and provenance tracking. It also monitors the platform’s own API endpoints for vulnerabilities — a design decision drawn from real-world AI system breaches.

Ken runs the detection engine, selecting and optimising a model stack that includes CNN-LSTM for deep pattern detection, Random Forest for classification, and a proprietary four-trigger hybrid engine covering statistical envelope, rate density, geometric spike, and physics-informed residual analysis.

Deb is the coordinating layer: routing tasks, assembling findings into structured briefings, and delivering them via dashboard, WhatsApp, or Signal.

“A single-model system gives you an answer,” Demarcus says. “Our agent team gives you a process: secure, detect, and brief. No black boxes. Every decision attributable.”

The multi-agent architecture is a deliberate departure from how most AI systems are designed. “Most AI systems are monolithic: one model does everything. That is brittle. When the model fails, it fails silently and completely.”

Explainability as architecture, not add-on

The word “explainability” gets thrown around liberally in AI marketing. Demarcus has built it into the foundation of how the system works.
When an alert fires, operators do not receive a generic “anomaly detected” flag. They see which of the four triggers fired, the exact numerical threshold crossed, the reasoning behind the decision, and the source data. In the battery thermal use case, an alert reads something like: Trigger D fired. Actual thermal rate: 4.2°C/min. Physics model predicted: 2.1°C/min. Residual: 3.2σ. Risk state: Watching brief (50 per cent). Recommended action: Reduce load in 45 seconds or critical state predicted.

This approach also addresses the hallucination problem that plagues large language model-based systems in safety-critical contexts. Marsham Edge does not rely on third-party LLM APIs for detection. The detection engine runs on customer infrastructure, using proprietary models grounded in statistical and physical laws that structurally eliminate generative ambiguity.

Also Read: Forget the cloud: Why AI is becoming the new heavy industry (and what investors must know)

A two-trigger gate further reduces false alarms: no single noisy sensor can trigger an alert. Two independent triggers must fire simultaneously before the system issues even a Watching Brief.

The battery problem nobody has solved

One of Marsham Edge’s most compelling use cases is early warning of lithium-ion battery thermal runaway, and Demarcus speaks about it with the urgency of someone who has witnessed it firsthand.

Thermal runaway is notoriously difficult to detect because the failure mode is exponential. By the time a conventional sensor hits its threshold, the reaction is frequently irreversible. Most industry tools monitor temperature thresholds and voltage drops, triggers that fire too late.

Marsham Edge’s approach fits a physics-informed energy-balance model (Newton’s Law of Cooling) to each battery’s individual thermal signature, then continuously compares the measured rate of temperature change against what physics predicts. Validated against datasets from the National Renewable Energy Laboratory (NREL), Sandia National Laboratories, and NASA, the platform demonstrated early-warning windows of 220 to 359 seconds ahead of standard hardware-level 80°C threshold alarms. “That is the difference between a controlled intervention and a fire,” Demarcus says flatly.

Deployed, validated, and winning hackathons

Although the startup is less than a year old, Marsham Edge already has live deployments. In May 2026, the full agent team completed an integration test against a synthetic OSINT dataset: Argo quarantined all four malformed records; Ken detected 18 of 18 campaign posts with zero false positives (F1 = 1.00); Deb delivered a structured analyst briefing in three minutes and seven seconds.

Shortly after, the platform was deployed on a live client dataset of 174 silica exposure measurements from an underground mining operation in New South Wales, Australia. Ken identified 31 exceedances — 17.8 per cent of the dataset — with a peak reading of 0.273 mg/m³, or 5.5 times the legal limit of 0.05 mg/m³. Argo flagged the client’s documented use of banned compressed air as a factor that elevated their prosecution risk from Category 2 to Category 1.

It is against this backdrop that Demarcus won the Epic Hackathon Singapore, competing against teams she describes as “half my age.”

“Younger founders often build fast and ask questions later. That is valuable. But in safety-critical environments, speed without accountability is dangerous,” she says. “The hackathon confirmed what I already believed: experience matters. It teaches you which signals are important and which are noise. The agents handle the noise. I handle the accountability.”

Building for the regulatory future

Demarcus is not merely solving today’s operational problems. She is positioning Marsham Edge at the convergence of three trends she sees as inevitable: mandated explainability under frameworks like the EU AI Act and Singapore’s AI Verify programme; the shift to edge and on-premise deployment in regulated industries unwilling to route sensitive data through third-party clouds; and the broader move from monolithic models to specialised agentic architectures.

“We are building for the regulatory future, not the regulatory present,” she says.

Also Read: How Asia’s factories are leading the way in industrial AI

The next stop is VivaTech Paris, where she intends to pursue sovereign cloud partners, defence AI integrators, and investors who grasp that explainability is fast becoming a compliance requirement rather than a product differentiator.

“What the global tech community should understand is this: Singapore is not just a financial hub. It is a defence and critical infrastructure nexus. We are building a platform that solves a universal problem, black-box AI in high-stakes environments, from a country that values security, sovereignty, and trust.”

One year in, with live deployments and independent validation already in hand, Marsham Edge is making a credible case that the next frontier in AI is not raw capability; it is accountability.

The post She watched her neighbour’s garage burn down. Now she’s building AI that explains itself before disaster strikes appeared first on e27.

Wall Street delivered a distinctly split performance on Tuesday, 16 June 2026, as investors aggressively rotated capital out of technology giants and into cyclical sectors. This massive shift sent the Dow Jones Industrial Average to its two consecutive record closes, pushing the index just a fraction away from the 52,000 milestone. Meanwhile, the S&P 500 and the Nasdaq Composite both finished in the red. These major indices paused their momentum after a massive rally on Monday. That previous surge stemmed directly from a breakthrough peace framework between the United States and Iran.

Geopolitical relief and a subsequent collapse in oil prices drove much of this market dislocation. Optimism surrounding a tentative deal to end the conflict between the United States and Iran pushed energy prices below US$80 a barrel. This marks the 1st time crude traded at those levels since March. Lower oil and transport costs immediately eased broader corporate inflation worries across the global economy and energy producers.

Brent Crude plunged 5.06 per cent to settle at US$78.96 per barrel. West Texas Intermediate slipped 5.82 per cent to close at US$76.05 per barrel as traders executed a rapid unwind of risk premiums. This energy deflation directly impacted government bonds. The United States 10-Year Treasury Yield held tight near monthly lows at 4.426 per cent. Softer oil numbers effectively blunted core inflation expectations and gave fixed-income markets a brief respite from the relentless pressure of rising consumer prices.

Also Read: Bitcoin’s major resistance sits in the US$67,000 to US$69,000 zone: What’s the next move?

This monetary uncertainty triggered a violent sector rotation across the equity markets. Money flowed swiftly away from semiconductor and artificial intelligence leaders commanding high valuations in the technology sector. Capital rerouted toward cyclical heavyweights, banking institutions, and manufacturing equities. The corporate winners and losers on Tuesday perfectly illustrate this dramatic pivot. SpaceX climbed 4.83 per cent to close at US$201.80. The stock briefly hit an intraday high of US$225.64. This surge following the initial public offering pushed the total market value of the aerospace company past Amazon.

Conversely, major artificial intelligence hardware players pulled back sharply. Advanced Micro Devices plummeted over seven per cent. Micron Technology dropped six per cent. Broadcom shed four per cent, and Nvidia gave up two per cent. The market routinely overvalues the current artificial intelligence hype cycle while ignoring the foundational infrastructure of true decentralisation. This mispricing creates incredible opportunities for those who understand the long-term trajectory of technological convergence and human-centric design.

The cryptocurrency market stabilised and turned green, shaking off weeks of aggressive capital outflows. Much like traditional equities, the broader digital asset ecosystem experienced a sharp relief bounce directly following the news of a preliminary United States and Iran ceasefire agreement. Market short liquidations reached US$373 million as traders forcefully closed their losing short positions. The Crypto Fear and Greed Index recovered significantly to 23, which indicates Fear. This represents a massive climb out of the extreme fear lows in the one-digit numbers from exactly one week prior.

I have always maintained that digital assets offer a superior form of speculative engagement compared to traditional stocks. The resilience of the crypto market during macroeconomic stress proves that decentralised networks possess intrinsic value beyond mere fiat speculation. Investors finally recognise the structural superiority of permissionless financial rails that operate independently of centralised banking hours.

Also Read: Why Bitcoin just surged past US$65,000 while oil crashed 4%

Bitcoin led this digital asset recovery, trading at US$66,449.38 with a gain of 0.9 per cent. The premier cryptocurrency experienced a brief intraday spike above US$67,000 following the Middle East peace framework announcement. Institutional investors maintain incredibly strong conviction despite the broader market volatility. MicroStrategy acquired another 1,587 BTC for US$100 million. This aggressive accumulation strategy by corporate treasuries signals a profound lack of faith in the traditional fiat banking system and corporate balance sheets.

I see this corporate behaviour as a validation of the core thesis behind decentralised digital scarcity from my position as a web3 founder. Traditional financial institutions and corporations quietly hedge against the very centralised monetary policies they publicly support. This hypocrisy underscores the fundamental flaw in the current global financial architecture and accelerates the migration toward decentralised alternatives. Smart investors now recognise that digital assets provide the ultimate hedge against systemic fiat failure and endless currency debasement.

The macroeconomic backdrop shifted further as the Warsh Federal Reserve meeting began. The Federal Open Market Committee kicked off its two-day policy meeting on Tuesday. Investors focus intently on the 1st press conference of incoming Federal Reserve Chairman Kevin Warsh taking place tomorrow. Market participants desperately search for signals on the future global monetary direction and monetary policy.

I watch these centralised monetary rituals with deep scepticism. What about you? 

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Singapore enterprises are deploying AI agents at the highest rate in Asia-Pacific, yet are also among the most likely to pull them back after going live, according to new research by communications tech company Sinch.

The report, titled The AI Production Paradox, found that 82 per cent of Singapore enterprises have rolled back or shut down a deployed AI agent, a rate eight percentage points above the global average. The finding is particularly striking given that Singapore simultaneously recorded the highest AI agent deployment rate in APAC at 72 per cent.

The study, based on an independent survey of 2,527 senior decision-makers across 10 countries and six industries, suggests that for many enterprises the central challenge around AI agents has shifted — from getting them into production to keeping them there.

“The real risk across APAC isn’t moving slowly, it’s scaling on infrastructure that can’t keep up,” said Wendy Johnstone, Executive Vice President, APAC at Sinch.

A regional pattern of high deployment, high failure

Singapore’s experience reflects a broader trend across Asia-Pacific. The region recorded the highest AI agent deployment rate globally, with 67 per cent of enterprises already operating AI agents in production — five percentage points above the global average. Yet 83 per cent of APAC enterprises have experienced an AI agent failure, the highest failure rate of any region surveyed and nine points above the global average.

Also Read: She watched her neighbour’s garage burn down. Now she’s building AI that explains itself before disaster strikes

Among the most immediate operational consequences, 45 per cent of APAC enterprises cited support team overload as the primary outcome when an AI agent fails. In Singapore, 44 per cent of enterprises reported the same. Given that one in three APAC enterprises sends more than 100 million messages per month, even a contained AI agent failure carries the potential to escalate quickly into broader disruption affecting customer satisfaction and brand trust.

Governance gap persists despite compliance focus

Despite 75 per cent of Singapore enterprises prioritising investment in trust, security and compliance, the research identified a significant governance shortfall. Only 27 per cent of Singapore enterprises report fully mature guardrails — the lowest figure in APAC and well below the global average of 35 per cent.

Across the region, the link between governance and AI advancement was found to be 48 per cent stronger than the global average, with enterprises that established proper governance before deployment recording better outcomes.

The research points to communications infrastructure as a critical but underserved factor in AI agent success. While 82 per cent of Singapore organisations rated high-performance infrastructure as essential or very important, just seven per cent said their current provider was fully meeting their needs — one of the lowest satisfaction figures among all markets surveyed.

As a result, 91 per cent of Singapore enterprises are currently evaluating new communications providers, five percentage points above the global average. On average, Singapore enterprises are planning AI agent deployment across 3.1 channels, with WhatsApp and web-based chatbots among the most common integration points.

Also Read: Vietnam’s biggest PE bet of 2025 was not on tech. It was on what 100M people eat every day

Notwithstanding the challenges, AI agents remain a clear priority. Some 40 per cent of Singapore enterprises plan to increase AI investment by more than 25 per cent compared to the prior year, with businesses focusing on selective, sustainable expansion rather than rapid scaling.

The post Singapore leads APAC in AI agent deployment but also in rollbacks, research finds appeared first on e27.

The table above is conservative. In markets where local payment methods are the primary way people transact: GoPay and OVO in Indonesia, Mada and STC Pay in Saudi Arabia, PromptPay in Thailand, an airline that accepts only international cards is structurally invisible to a large portion of its addressable market. That is not a payment problem. That is a market access problem.

The complexity airlines were not built to handle

To understand why airline payments fail so often, you need to understand the unique complexity stack airlines operate within. No other vertical faces quite this combination.

Airlines sell globally but settle locally. A single booking may involve a passenger in Singapore, an origin airport in Australia, a destination in Japan, a codeshare partner in the Middle East, and interline settlement through IATA’s BSP. The payment that funds all of this needs to work flawlessly across multiple currencies, regulatory regimes, and financial relationships — in real time.

Also Read: The US$0.20 payment that could rewire Asia’s financial rails

At the same time, the distribution landscape has fragmented. Airlines now sell through their own direct channels, through OTAs, through NDC-connected travel management companies, and through GDS. Each channel has different payment capabilities, different fraud profiles, and different customer expectations. A payment infrastructure designed for one channel will fail on the others.

What smart retry alone can recover

Here is something that surprises most airline finance and revenue leaders when they first see it: 20-40 per cent of declined transactions are recoverable. The customer was willing to pay. The card was valid. The money was there. The system just failed to capture it.

Smart retry logic, the ability to automatically reattempt a failed transaction through a different acquirer, with modified parameters, within seconds, is table stakes in e-commerce. It is standard practice at any sophisticated online retailer. In aviation, it remains uncommon.

The reason is integration complexity. Routing a transaction to a different acquirer requires relationships with multiple PSPs, a real-time decision engine that can assess why a transaction failed and select the optimal retry path, and integration with the PSS in a way that does not disrupt the booking flow. Building all of that in-house is a multi-year engineering project. Most airlines do not have the team for it.

This is precisely where orchestration changes the equation. A payment orchestration layer sits between the airline’s booking system and the payment ecosystem, providing a single integration point that unlocks access to multiple acquirers, retry intelligence, and local payment methods simultaneously. The airline gets years of infrastructure in weeks of integration.

The local payment method gap is a market access problem

In 2024, approximately 48 per cent of e-commerce transactions in Southeast Asia were completed using local payment methods: wallets, bank transfers, and local card schemes, rather than international cards. In Saudi Arabia and the UAE, the share of local payment instruments has grown substantially as domestic schemes like Mada have matured.

An airline that operates routes into these markets but does not support local payment methods is not just leaving money on the table. It is effectively pricing itself out of the market for a growing segment of travellers who prefer or exclusively use local payment instruments.

The integration challenge is real. Adding GoPay requires a different technical integration than adding Mada. Each has its own API, its own settlement model, its own compliance requirements. For an airline managing a single PSS integration, adding ten local payment methods across five markets represents a significant engineering investment – and ongoing maintenance overhead.

The orchestration model solves this with a hub-and-spoke architecture: the airline integrates once with the orchestration layer, which maintains and manages all individual payment method integrations. When regulations change or a new wallet gains market share, the orchestration layer updates. The airline does not need to re-engineer its checkout.

Also Read: The next phase of payments in Southeast Asia is about more than moving money

3DS: The necessary friction that became unnecessary friction

Strong Customer Authentication (SCA) and 3DS2 are necessary tools. They reduce fraud and protect airlines from chargebacks. But calibrated incorrectly, they become conversion killers.

The core tension is this: 3DS challenges add friction to the checkout flow. Every additional step: a redirect, an OTP, an app-based authentication, creates an opportunity for abandonment. Studies across e-commerce consistently show that conversion drops 10–20 per cent when a 3DS challenge is presented versus when it is not.

The solution is not to remove the 3DS. It is to apply it intelligently. Modern 3DS2 supports frictionless flows – where the issuer authenticates the transaction in the background without user interaction – for low-risk transactions. The trigger for a frictionless flow is rich data: device fingerprinting, transaction history, and behavioural signals. An airline that passes comprehensive contextual data through the 3DS process can dramatically increase its frictionless rate without increasing fraud exposure.

Most airline payment systems do not pass this data. They send the minimum required fields and accept whatever authentication outcome comes back. The result is unnecessary challenges to legitimate transactions, unnecessary abandonment, and unnecessary revenue loss.

The orchestration answer

Payment orchestration is not a new concept in e-commerce. The world’s leading online businesses – including several of the largest travel OTAs – have been running orchestration layers for years. For airlines, it is still early. But the early movers are seeing results.

What a mature orchestration layer delivers for an airline:

• Multi-acquirer routing with automatic failover – no single point of payment failure
• Intelligent retry that recovers 20-40 per cent of initially declined transactions
• Local payment method coverage across all key markets via a single integration
• Market-specific 3DS logic that maximises frictionless authentication
• Real-time analytics on payment performance by route, market, and payment method
• Compliance management across different regulatory regimes

The shift from a single-acquirer model to an orchestrated payment infrastructure is not just a technology upgrade. It is a revenue recovery exercise. For a mid-sized airline processing US$2 billion in annual ticket revenue, a three per cent improvement in payment conversion rate is US$60 million. A one per cent reduction in the decline rate on cross-border transactions is US$20 million. These are not speculative numbers – they are the figures airlines are actually realising when they make the switch.

Also Read: Asia’s student boom is exposing a hidden weakness in global payments

What airlines should do now

The path forward is not complicated, but it requires leadership alignment between finance, technology, and commercial teams – groups that do not always sit at the same table when payment infrastructure decisions are made.

First: audit your current payment performance. What is your decline rate by market? By payment method? By booking channel? Most airlines cannot answer these questions with granularity because their PSS reporting was not built to surface payment intelligence. If you cannot measure it, you cannot improve it.

Second: map your addressable market against your payment method coverage. If you fly routes into Indonesia, Malaysia, Saudi Arabia, or Thailand, and you do not support the dominant local payment methods in those markets, you have a quantifiable market access gap. That gap has a dollar value. Make it visible to your commercial leadership.

Third: evaluate orchestration as a strategic capability, not a vendor conversation. The question is not which payment gateway to work with. The question is whether your payment infrastructure is architected for resilience, intelligence, and flexibility – or whether you are one acquirer outage away from a catastrophic revenue event.

The airlines that win the next decade will not just be the ones with the best routes or the most frequent flyer programmes. They will be the ones who can sell to anyone, anywhere, in any payment method, without losing the transaction. That capability is available today. The question is whether you will build it before your competitor does.

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The post Why airlines lose more revenue to payment failures than to empty seats appeared first on e27.