For retailers, artificial intelligence is only as useful as the data it can read. A chatbot that cannot understand product attributes, stock availability, customer intent, or the difference between two near-identical SKUs is unlikely to move the needle on sales. That is the gap Singapore-based Graas is trying to close with its latest fundraise and acquisition.

Graas, a retail commerce AI company, has raised US$17 million in a Series B round and acquired Trustana, a Singapore-based product-data platform. The round was led by LemmaTree, an investment firm founded by Singapore state investor Temasek, with participation from Integra Partners, Tin Men Capital, The Xander Group, IncredWealth and Orzon.

Also Read: AI shopping companions and the talent reset in retail

The financial terms of the Trustana acquisition were not disclosed.

Founded in 2022, Graas builds AI tools for retail commerce, drawing on more than US$1 billion in live commerce transactions processed through its platform.

Trustana, founded in 2020, helps retailers and distributors automate the enrichment and structuring of product information, the less glamorous but critical layer that determines whether a product can be discovered, compared, recommended, and sold accurately across channels.

Graas said Trustana’s product-data enrichment capabilities will be integrated into its Commerce Knowledge Graph, the underlying database that powers its AI agents for retail clients. In simple terms, a knowledge graph connects different pieces of information (products, customers, inventory, transactions, content, pricing, and behaviour) so that AI systems can make better decisions.

For a retailer, that could mean an AI agent which does not merely answer a customer’s question, but understands whether a product is in stock, whether a similar item has a higher margin, what the customer has bought before, and how to recommend the right option across chat, voice, or image-based search.

Why product data matters

The Trustana deal points to a wider shift in retail AI. Over the past year, much of the attention has gone to customer-facing generative AI tools: shopping assistants, automated ads, AI-written product descriptions, and conversational search. But these tools break down quickly when the underlying data is incomplete, inconsistent, or scattered across different systems.

This is a common problem in Southeast Asia, where retail is fragmented across online marketplaces, brand websites, social commerce, supermarkets, distributors, and general trade channels. A consumer may discover a product on TikTok, compare it on Shopee or Lazada, buy it through a brand site, and later repurchase it at a neighbourhood store. For large brands, stitching this journey together is difficult.

The challenge is even sharper for companies operating across multiple markets. Product catalogues are often maintained in different languages, formats, and systems. The same item can carry different descriptions, categories, pack sizes, and images depending on the sales channel. Without cleaning and structuring that data, AI agents risk producing poor recommendations or, worse, misleading answers.

This is where Trustana fits into Graas’ broader pitch. By adding Trustana’s product-data layer to customer and inventory information, Graas wants to build AI agents that can support both e-commerce and offline retail channels.

Prem Bhatia, co-founder and CEO of Graas, said bringing product, customer, and inventory data into a single system would allow AI agents to drive sales in both e-commerce and general trade.

Also Read: More choices, less hassle: Unlocking retail magic with AI and tech

That focus on general trade is significant in Southeast Asia. Despite the rise of digital commerce, a large share of consumer goods sales in the region still happens through small shops, distributors, and offline channels. For global brands such as Unilever or Puma, AI tools that only optimise marketplace ads or online storefronts solve only part of the problem.

A broader customer base

The acquisition also gives Graas access to Trustana’s customer relationships. Trustana’s clients include David Jones, Chemist Warehouse, and Toys”R”Us. Graas already serves brands including Unilever, Puma, and Schneider Electric, with operations across Australia, Southeast Asia, and the Gulf.

This regional spread matters. Retailers in mature markets such as Australia may have more structured digital operations, while Southeast Asian markets tend to involve more fragmented distribution and marketplace-led commerce. The Gulf, meanwhile, has seen rising investment in retail digitisation, particularly among large consumer brands and mall-based retail groups.

For Graas, combining these markets could offer a wider base of commerce data and use cases. The company says its platform already draws on more than US$1 billion in live commerce transactions, a figure that gives its AI systems more context on how consumers browse, compare, and buy.

Rebecca Xing, CEO of Trustana, said the combination would help the company accelerate growth and deliver more to its global customers.

Glenn Gore, CEO of LemmaTree, said the combined platform is positioned to help retailers make use of agentic AI — a term used to describe AI systems that can take actions on behalf of users, rather than simply generate text or analysis.

The term is becoming common in enterprise software, though it remains loosely defined. In retail, an agentic AI system could monitor inventory, recommend pricing changes, generate product content, suggest campaign adjustments, or respond to customer queries with purchase options. The practical value depends on how well the system is connected to real-time business data.

The competitive field

Graas is operating in a crowded and fast-changing market. Globally, large software providers such as Salesforce, Adobe, Shopify, Bloomreach, Algolia, and Dynamic Yield are embedding AI deeper into commerce, personalisation, search, and customer engagement tools. In Asia, companies such as Insider also compete in customer experience and marketing automation, while marketplace operators and e-commerce enablers offer their own analytics and optimisation layers.

Graas’ differentiation appears to lie in its focus on retail commerce data across multiple channels, rather than only storefront software or marketing automation. The Trustana acquisition strengthens that positioning by adding product intelligence, a foundational layer that many AI commerce tools still depend on but do not always control directly.

Still, execution will be the test. Enterprise retail customers are often slow to replace core systems, and many already use a mix of ERP, CRM, marketplace, and analytics platforms. To win larger accounts, Graas will need to show that its AI agents can plug into existing workflows, improve sales or margins, and reduce the manual work that typically sits behind catalogue management and commerce operations.

Singapore’s AI commerce play

The deal also reflects Singapore’s continued role as a base for regional enterprise technology companies. The city-state’s proximity to Southeast Asian markets, access to capital, and concentration of regional headquarters make it a natural launchpad for companies selling to large brands across Asia-Pacific and the Middle East.

For investors, retail AI offers a large but demanding opportunity. Consumer brands are under pressure to grow across more channels while managing tighter margins and higher customer expectations. AI promises efficiency, but retailers are increasingly looking beyond experiments and pilots. They want systems that can produce measurable commercial outcomes.

Also Read: Revolutionising retail: A blueprint for future success

Graas’ fundraise and acquisition suggest a bet that the next phase of retail AI will not be won by flashy interfaces alone. It will depend on whether companies can organise messy commerce data well enough for AI agents to act on it reliably.

In a region where retail remains both deeply digital and stubbornly offline, that may be the harder problem — and the more valuable one to solve.

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Cybersecurity teams entered the second half of 2026 with little sign of relief. Global organisations faced an average of 2,336 cyber attacks per week in July, up 3 per cent from June and 16 per cent from a year earlier, according to new data from Check Point Research.

The figures point to a threat environment that is not merely growing in volume, but widening in scope. Traditional attack channels such as email remain persistent, ransomware groups appear to have regained momentum, and generative AI tools are creating fresh data leakage risks inside companies faster than many security policies can catch up.

Also Read: Southeast Asia’s cyber boom is fuelled by fear—and AI

For Southeast Asian startups and digital businesses, the findings land at an uncomfortable moment. The region’s companies are adopting AI, cloud software and cross-border digital operations at speed, often with lean security teams and fragmented tooling. That combination can create the kind of gaps attackers look for: exposed credentials, unmonitored data flows, vulnerable suppliers and employees using new tools before governance catches up.

“July’s data shows that cyber risk is accumulating across multiple fronts at once,” said Omer Dembinsky, Data Research Manager at Check Point Research. “Attack volumes continue to rise, ransomware has accelerated sharply, and GenAI exposure is now part of daily business activity.”

Education and government remain in the firing line

The education sector remained the most attacked globally, with an average of 4,848 weekly attacks per organisation in July, a 14 per cent increase year on year. Government followed with 3,044 attacks, while telecommunications recorded 2,927.

The prominence of education is not surprising. Schools, universities and training institutions often hold large volumes of personal data, run sprawling IT networks, and operate with uneven cybersecurity budgets. In Southeast Asia, where governments have pushed digital learning platforms, online admissions and student management systems, these institutions can be attractive targets for both data theft and disruption.

Energy and utilities also saw a sharp increase, rising 20 per cent year on year to 2,759 weekly attacks per organisation. Hospitality, travel and recreation entered the top five with 2,614 attacks, up 28 per cent.

That matters for Southeast Asia, where tourism has rebounded strongly since the pandemic and travel operators have become deeply dependent on digital booking, payment and identity systems. A breach in this sector can quickly spill across customers, payment providers, loyalty programmes and third-party booking platforms.

APAC remains one of the world’s most attacked regions

Latin America recorded the highest attack volume in July, with 3,561 weekly attacks per organisation, up 19 per cent year on year. Asia Pacific followed closely at 3,316 attacks, ahead of Africa at 3,237.

Europe stood out for the pace of increase, rising 18 per cent year on year to 2,051 attacks per organisation. North America saw a 9 per cent increase to 1,613.

For Southeast Asia, APAC’s high ranking reflects a familiar structural issue. The region is home to fast-growing digital economies, but cybersecurity maturity varies widely between markets and sectors. A fintech in Singapore, an e-commerce platform in Indonesia, a logistics company in Vietnam and a hospital network in the Philippines may all be part of the same digital supply chain, but operate under different standards, budgets and regulatory pressures.

Also Read: What AI safety researchers actually worry about

This unevenness is a particular concern for startups. Many young companies rely on cloud platforms, software-as-a-service tools and outsourced development teams from day one. These choices help them scale quickly, but also widen the attack surface if access controls, vendor reviews and incident response plans are treated as later-stage concerns.

GenAI turns into a daily data risk

Perhaps the most modern risk in Check Point’s July data concerns generative AI. The research found that one in every 36 enterprise prompts carried a high risk of sensitive data leakage. Among organisations that regularly use GenAI, 88 per cent were affected by high-risk prompt activity.

The issue is not simply that employees are experimenting with chatbots. It is what they are putting into them. Check Point found that 22 per cent of prompts contained potentially sensitive information. Personal data appeared in 70 per cent of affected organisations, while financial data and network or IT infrastructure information each appeared in 68 per cent.

On average, organisations used eight GenAI tools, with users generating 95 prompts. In practical terms, this means employees may be feeding customer records, internal financial details, source code, contracts, credentials or system architecture into tools that were not approved or monitored by security teams.

For Southeast Asian startups, the risk is acute because GenAI has moved quickly from novelty to workflow. Founders use it to draft investor updates, developers use it to debug code, sales teams use it to summarise customer calls, and operations teams use it to process documents. Without clear rules, a productivity tool can become an unintentional data export channel.

The challenge is to govern AI use without blocking it outright. Companies will need policies that define what can and cannot be entered into public tools, technical controls to detect sensitive data in prompts, and safer enterprise-grade AI environments for teams that need to work with confidential information.

Email remains the old reliable route for attackers

Even as AI creates new risks, email continues to do what it has always done for attackers: provide a cheap, scalable entry point.

Check Point found that one in every 128 emails, or 0.78 per cent, was classified as phishing in July. Another 20 per cent fell into unwanted or risky categories such as graymail, spam and suspicious messages. Africa had the highest phishing rate, at one in every 106 emails, followed by North America at one in every 117.

Phishing remains effective because it targets people rather than systems. A single fake invoice, delivery notice, password reset request or investor email can be enough to trigger credential theft, malware installation or business email compromise.

In Southeast Asia, where companies often work across languages, currencies and jurisdictions, the room for deception is wide. A fraudulent supplier email or payment instruction can be difficult to spot when teams are already managing regional vendors, remote staff and multiple messaging channels.

Ransomware breaks from its earlier pattern

The clearest shift in July was ransomware. Reported ransomware attacks reached 964, up 49 per cent from June and 87 per cent compared with July 2025. That marked a break from the first half of 2026, when monthly activity averaged around 672 incidents.

Business services accounted for 32.5 per cent of reported victims, followed by industrial manufacturing at 14.4 per cent and consumer goods and services at 13.4 per cent. North America remained the most affected region, accounting for 45 per cent of incidents, while Europe followed at 28 per cent and APAC at 17 per cent.

The United States dominated the country-level victim count with 39.4 per cent of reported attacks, ahead of Germany, Canada, the United Kingdom and Italy.

Ransomware data based on published victims can undercount the real scale of incidents, as not every attack is disclosed or listed by criminal groups. Still, the July jump suggests attackers are finding enough success to sustain and expand operations.

The most active groups in July were The Gentlemen and Qilin, each responsible for 14 per cent of published attacks. DeadLock followed with 10 per cent and 97 reported victims, underlining how fluid the ransomware ecosystem remains as groups rebrand, fragment or compete for targets.

Also Read: Thailand is suddenly on the frontline of a new ransomware wave

For founders and operators, the takeaway is blunt: cybersecurity is no longer just an enterprise IT problem. It is a business continuity issue. As attack volumes rise and AI reshapes both productivity and exposure, companies that treat security as an afterthought may find that the cost of catching up arrives all at once.

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For many finance chiefs, the first wave of AI was about testing tools: automating reports, speeding up reconciliation, or asking software to spot anomalies in spreadsheets. In Singapore, that phase is quickly giving way to a more difficult question: how to make AI work across the messy reality of regional finance operations.

A new Forrester Consulting study commissioned by Airwallex suggests Singapore is among the more advanced markets globally in operationalising AI within finance functions. But it also points to a constraint that will be familiar to many Southeast Asian companies expanding across borders: fragmented systems, inconsistent data, and legacy workflows are now bigger obstacles than access to AI models themselves.

Also Read: From KYC to KYA: how AI agents are reshaping payment risk

The study surveyed more than 1,200 finance decision-makers across 11 markets, including Singapore, and was launched as part of Airwallex’s Business Builders programme, a Singapore initiative featuring founders and finance leaders from companies such as Endowus, StaffAny, CardUp, GlobalTix, Peakflo, Polybee, Chronos Agency, Stryv and Pitstop Tyres.

The findings capture a shift in the region’s AI conversation. Adoption is no longer the headline. Execution is.

The AI budget is still rising

Singapore finance leaders are not pulling back from AI. According to the study, 96 per cent expect investment in AI-powered finance to increase over the next 12 months. That figure reflects how quickly AI has moved from a side experiment to a core operating priority.

In finance teams, AI is being used for tasks such as bookkeeping, reporting, forecasting, fraud detection, compliance checks and scenario modelling. These are areas where speed and accuracy matter, but where human teams are often slowed down by manual processes and scattered data.

The appeal is clear. In Southeast Asia, even relatively young companies often operate across multiple markets, currencies, banks, payment rails and regulatory regimes. A Singapore-headquartered SaaS, fintech, travel or e-commerce startup may collect revenue in Indonesia, pay vendors in Vietnam, hire teams in the Philippines and raise capital from overseas investors. That creates a level of financial complexity that spreadsheets and disconnected tools struggle to manage.

AI can help, but only if it can see the full picture.

That is where many finance teams are getting stuck.

Fragmented data is the real bottleneck

While AI adoption is high, scaling it remains harder. In Singapore, 64 per cent of finance leaders identified fragmented or inconsistent data across disconnected systems as a core barrier to scaling AI. Globally, 65 per cent cited the same issue.

This matters because AI systems depend on clean, timely and connected information. If transaction data sits in one system, procurement in another, payroll somewhere else, and regional subsidiaries use different reporting formats, AI can only produce partial insights. In some cases, it may automate bad assumptions faster.

The study also found that 68 per cent of Singapore respondents said their finance workflows are only partially digitalised, while 53 per cent said data either flows inconsistently across finance platforms or remains largely siloed.

For a region such as Southeast Asia, this is not a minor operational issue. Many companies expand market by market, often adding tools as they go. A payment provider may be chosen for one country, an accounting platform for another, and a separate expense tool for a newly opened office. What works in the early stages can become a constraint as the business grows.

Arnold Chan, General Manager for Asia Pacific at Airwallex, framed the challenge directly: “Businesses are no longer asking whether to invest in AI. They’re asking how to make AI work at scale.”

Also Read: The language tax: Why AI skips your startup when buyers ask in Thai

He added that the biggest obstacle is not access to AI models, but the financial systems beneath them. “Businesses that connect their financial data, workflows and operations will be far better positioned to move beyond isolated AI use cases towards more intelligent, autonomous finance.”

Singapore is ahead, but not fully autonomous

The study suggests Singapore finance teams are further along than their global peers in allowing AI to run parts of finance operations with limited human involvement.

Eighteen per cent of Singapore respondents said AI already runs autonomously with minimal human input across finance workflows, compared with 11 per cent globally. In record-to-report processes, which include bookkeeping, closing and reporting, 27 per cent of Singapore finance leaders said AI runs autonomously, compared with 14 per cent globally.

That does not mean finance departments are handing over decision-making wholesale. Much of the near-term opportunity remains practical rather than futuristic.

In Singapore, 64 per cent of respondents expect AI to generate cash-flow forecasts and what-if analyses that recommend actions for humans to decide on within the next year. Globally, the figure is 51 per cent. Another 43 per cent of Singapore finance leaders expect AI to identify patterns, trends and root causes while leaving decisions to people.

This distinction is important. In finance, especially in regulated sectors such as fintech, wealth management and payments, full automation carries risks. AI-generated forecasts may be useful, but companies still need accountability, audit trails and human judgement when decisions affect cash, compliance or customers.

For Southeast Asian startups, where capital efficiency has become a sharper priority since the funding slowdown, better forecasting can still be valuable. Knowing earlier when working capital will tighten, when supplier payments may clash with payroll, or when regional revenue is drifting from plan can give management teams more room to act.

Talent becomes part of the infrastructure

The study also points to a second layer of readiness: people.

Singapore appears ahead here too. Twenty-seven per cent of finance leaders said their organisations have implemented structured, enterprise-wide AI talent strategies covering role redesign, certifications and hiring, compared with 15 per cent globally. Another 25 per cent said they have in-house AI development capabilities within or closely aligned to finance, versus 16 per cent globally.

This is significant because AI in finance is not simply a technology upgrade. It changes how teams work. Finance professionals may need to understand how to validate AI outputs, design workflows, question recommendations and work with engineering or data teams. The role moves from compiling information to interpreting and governing it.

At the same time, the study suggests not every company wants to build everything internally. Sixty-six per cent of Singapore finance leaders expect to use a hybrid model over the next year, combining in-house expertise with external providers. The proportion planning to build AI entirely in-house is expected to fall from 32 per cent today to 17 per cent.

That reflects a pragmatic reality. Even well-funded companies may not want to maintain large internal AI teams for finance alone. The more likely model is a mix of finance platforms, internal data capability and external specialists.

What this means for Southeast Asian companies

The broader lesson is that AI advantage in finance may depend less on who adopts the newest tool and more on who fixes the foundations first.

For startups and growth companies in Southeast Asia, this can be uncomfortable. Infrastructure work rarely attracts the same attention as product launches or fundraising rounds. But connected finance systems can determine whether AI becomes useful in daily decision-making or remains trapped in isolated pilots.

Also Read: From chatbots to payment agents: AI’s next role in SEA commerce

Singapore’s position as a regional headquarters market gives it a natural lead. Many companies base finance, strategy and investor relations teams in the city-state while operating across the rest of Southeast Asia. That makes Singapore a testing ground for AI-enabled finance models that may later be applied across more fragmented regional markets.

The next year will show whether companies can turn AI investment into operational change. The money is flowing, and the tools are improving. The harder task is making sure the data, systems and people are ready for them.

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Few words in business are abused as generously as ecosystem.

It appears in investor decks, product strategy documents, market narratives, and executive speeches with almost magical confidence. A company launches adjacent tools, adds integrations, bundles workflows, introduces a marketplace tab, and suddenly begins speaking as though it has crossed into a higher strategic category. The implication is always the same. This is no longer just a product. This is now a platform. This is now an ecosystem. This is now a position of structural power.

Most of the time, it is not.

What many companies call an ecosystem is simply a feature bundle with better language around it. It is a larger product surface, not a different market role. It may be useful. It may even be commercially smart. But usefulness is not the same as platform power, and adjacency is not the same as ecosystem formation.

An ecosystem begins when value creation is no longer controlled only by your roadmap

This is the simplest way to separate platform reality from platform theatre.

If nearly all meaningful value still comes from what your internal teams choose to build, package, release, and sell, then you do not yet have an ecosystem. You have a company with an expanded product portfolio. That may be a good business. It is not the same thing.

An ecosystem starts when external actors begin creating value that is meaningful to customers and meaningful to themselves because your platform exists. That means partners, developers, service providers, data contributors, implementation specialists, operators, or adjacent businesses are not merely attaching themselves to your marketing story. They are making decisions, investments, and in some cases their own commercial bets based on your presence in the market.

That is where the strategic category changes. The centre of gravity shifts from what you build to what others can build, offer, sell, certify, customise, govern, or depend on because your system provides the base layer.

This is why so many claimed ecosystems are overstated. The company still owns the proposition, owns the commercial model, owns the roadmap, owns the customer relationship, and owns nearly every meaningful form of innovation. Everyone else is decorative. They may integrate, implement, or resell, but they are not genuinely extending the market in a way that creates new value beyond the vendor’s own design.

Also Read: Why money won’t save Bangladesh’s startups: The ecosystem readiness crisis

Feature breadth is not the same as market orchestration

Many firms confuse having more things with holding a more important position.

The logic usually sounds persuasive at first. We now offer workflow A, workflow B, analytics, automation, collaboration, reporting, and compliance in one environment. Customers use more of our modules. We have integrations with major third parties. We have a partner page. We are becoming the centre of the ecosystem.

Perhaps. But perhaps not.

Feature breadth tells you that the company is occupying more use cases. It does not tell you whether the market is beginning to organise around the company as a coordinating layer. Those are very different conditions.

The real test is whether others can build serious economic logic on top of you

Can another business create durable economics because your platform exists, without simply acting as your implementation arm or distribution helper?

Not can they list themselves in a marketplace. Not can they complete an API connection. Not can they appear in a partner brochure. Can they build real business logic around your platform?

Can they specialise around it? Can they innovate on top of it? Can they develop differentiated offers because of it? Can they make investments that make sense only if your platform continues to matter? Can they gain customers, revenue, data, reputation, or operating leverage through participation that is not entirely controlled by your next release cycle?

Real platforms create politics, fake ones create packaging

There is a harder truth here that many executives would rather avoid.

A genuine ecosystem is not just larger. It is more difficult to govern. Once external actors begin relying on your platform for their own outcomes, you no longer have the luxury of pure product thinking. You now have politics.

You have to decide who gets access and on what terms. You have to decide how disputes are handled. You have to decide whether the platform favours its own products over third parties. You have to decide how standards evolve, who bears integration cost, how abuse is controlled, what quality thresholds apply, how data rights work, and what happens when the platform’s own interests conflict with those of participants building on top of it.

Also Read: Southeast Asia’s investors are sleeping on a US$2B ecosystem next door

Many ecosystem claims are really attempts to borrow strategic prestige

Part of the reason this language spreads so easily is that platform sounds like a more powerful category than product.

A product sounds finite. A platform sounds expansive. A product competes on features. A platform shapes markets. A product can be replaced. A platform becomes infrastructure. Leaders know this, investors know this, and the language becomes attractive very quickly.

So firms start narrating themselves upwards.

A company with adjacent modules begins to speak as though it has become a platform. A firm with a few partners begins to imply network effects. A vendor with bundled workflows starts describing market orchestration. The ambition may be genuine, but the language often outruns the operating reality.

The hidden issue is whether the company is willing to surrender control

Many firms say they want ecosystem dynamics, but what they really want is ecosystem valuation without ecosystem loss of control. They want others to extend the product, increase reach, add use cases, and create market energy, while the company still decides everything that matters.

That tension usually sits at the heart of the illusion.

A real platform has to surrender something. It has to allow external actors enough room to create meaningful value. It has to tolerate less central control over the total experience. It has to accept that innovation, customer intimacy, and even some forms of commercial power will now exist outside its direct management. It has to govern rather than simply command.

A bundle can still be a very good strategy, it just is not the same strategy

There is nothing weak or unserious about building a tightly integrated product suite. In many markets, that is exactly the right move. Customers may prefer one accountable vendor, cleaner workflows, faster procurement, simpler support, and less complexity. A broader feature set can deepen retention, increase share of wallet, and improve strategic relevance without any need for ecosystem theatre.

The problem is not bundling. The problem is pretending bundling and platform formation are the same thing.

They are not.

A bundle strategy is about offering more direct value yourself. A platform strategy is about enabling and governing value creation by others. A bundle strategy can be highly profitable and highly defensible. But it should be understood honestly, because the operational demands, investment logic, partner model, governance model, and eventual sources of power are different.

The company that confuses these paths usually ends up doing both badly. It never fully commits to platform openness, yet it also stops treating product coherence as its true strategic centre. It starts speaking like an orchestrator while operating like a suite vendor. That gap becomes visible over time.

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In a city where a five-minute delay can be the difference between keeping or losing a customer, ride-hailing is often won in the invisible layer behind the app: maps, traffic data, dispatch logic and estimated arrival times.

Singapore-based Ryde is now trying to improve that layer through a strategic partnership with HERE Technologies, the mapping and location data company. The two companies said in a joint statement that Ryde has integrated HERE Location Services into its platform to improve driver matching, routing accuracy and ETA predictions across Singapore.

Also Read: HERE Technologies leads UNL’s US$4.5M funding to ‘pixelise’ the physical world

The deal is not about adding another button to Ryde’s app. It is about making the core ride-hailing experience less uncertain: assigning the right driver, choosing a more realistic route, and telling riders more accurately when their car will arrive.

For Ryde, which is listed on the NYSE American under the ticker RYDE, the partnership comes as competition in Singapore’s mobility market remains intense. For HERE, it is another example of how location intelligence is becoming a key infrastructure layer for transport, logistics and quick commerce platforms in Southeast Asia.

Why routing still matters in a small city

At first glance, Singapore may seem like a relatively easy market for routing technology. It is geographically compact, highly mapped and supported by strong public infrastructure. But the reality for ride-hailing operators is more complicated.

Traffic conditions can shift quickly around expressways, Central Business Districts, schools, malls, industrial estates and housing towns. Roadworks, peak-hour congestion, rain and event-related traffic can all distort estimated arrival times. In ride-hailing, those distortions matter because the platform has to make decisions before the trip begins.

If a system assigns a driver who appears close on the map but is separated by a difficult junction, a congested slip road or a slow-moving arterial route, the rider waits longer and the driver loses time. Multiply that across thousands of trips, and small mapping errors can become operational costs.

Ryde said the integration of HERE’s routing engine and real-time traffic intelligence is aimed at improving dispatch decisions, ETA predictions and navigation through changing road conditions. HERE’s tools use live traffic data and route optimisation algorithms to support more accurate trip planning.

“At Ryde, we’re constantly investing in technologies that improve every journey for both riders and drivers,” said Ryde CTO Nitin Dolli. “Our partnership with HERE strengthens the intelligence behind our platform, enabling more accurate routing, smarter driver allocation and better ETA predictions.”

The hidden economics of better ETAs

For riders, ETA accuracy is a convenience issue. For platforms and drivers, it is also an economic one.

An inaccurate ETA can lead to cancellations, lower trust and poorer driver utilisation. If a driver is sent on a longer-than-expected pickup route, the time spent reaching the passenger is time not spent completing paid trips. If riders are repeatedly told that a vehicle is arriving sooner than it realistically can, they may switch to another platform.

This is why ride-hailing companies invest heavily in what may look like routine back-end upgrades. Driver supply, demand forecasting, route calculation and pricing are all linked. Better routing can help platforms reduce idle time, improve matching and make the app feel more reliable without necessarily increasing the number of vehicles on the road.

In Singapore, that is particularly important because the private-hire and taxi market operates within a tightly managed transport environment. Unlike some larger regional markets where platforms can scale supply more aggressively, Singapore’s vehicle population is shaped by high ownership costs, regulatory controls and strong public transport alternatives. Platforms therefore have to compete not only on price and incentives, but also on reliability.

Also Read: Inside HERE Technologies’ strategy to engage Southeast Asia’s decision-makers

HERE’s Southeast Asia and India General Manager Abhijit Sengupta said ride-hailing platforms depend on accurate location intelligence to keep people and businesses moving. He added that the partnership with Ryde is meant to improve driver utilisation, reduce uncertainty and deliver a better end-user experience.

Ryde’s broader mobility play

Founded in Singapore in 2014, Ryde began with carpooling and has since expanded into private-hire rides, taxis and delivery. The company describes itself as a “super mobility app”, although in practice it operates in a market where the dominant players have much broader ecosystems spanning payments, food delivery, advertising and financial services.

One of Ryde’s key differentiators has been its 0 per cent commission model for private-hire and taxi drivers. Instead of taking a cut from every completed ride in the way many ride-hailing platforms do, the company has sought to position itself as more driver-friendly. That strategy can help attract and retain supply, but it also means Ryde has to be disciplined about other parts of its business model, including technology costs, operational efficiency and customer retention.

The HERE partnership fits into that context. A smaller ride-hailing player cannot always outspend larger rivals on subsidies or marketing. But it can compete by making its service more dependable in specific markets. In Singapore, where users often compare wait times across multiple apps before booking, even marginal gains in pickup accuracy can matter.

The collaboration also reflects a wider shift in Southeast Asian mobility. Ride-hailing platforms are increasingly expected to support adjacent services such as parcel delivery, food delivery and quick commerce, all of which rely on precise location data. A routing engine that works well for passenger transport can also help in dispatching couriers or optimising delivery routes, although Ryde and HERE have framed the current announcement mainly around ride-hailing performance.

A crowded field

Ryde’s most obvious competitor in Singapore is Grab, the region’s largest ride-hailing and delivery platform, which has deep consumer reach and a broad driver network. Gojek, owned by GoTo, also remains a major mobility brand in the city, though its regional footprint has been shaped by strategic pullbacks and market-by-market competition. Local and regional alternatives include TADA, which has promoted a zero-commission approach, as well as ComfortDelGro’s CDG Zig app, backed by Singapore’s largest taxi operator.

Globally, the broader ride-hailing sector is shaped by companies such as Uber, Lyft, Didi and Bolt, though not all operate in Singapore. For Ryde, the challenge is not simply to match those companies feature by feature. It is to carve out a durable position in a market where scale, driver liquidity, trust and app reliability are closely intertwined.

Location data as infrastructure

The partnership also says something about the role of mapping companies in the current phase of mobility. In the early years of ride-hailing, much of the public attention was on consumer adoption, driver recruitment and regulatory battles. Today, more of the competition is happening inside the routing stack.

Maps are no longer static reference tools. They are live systems that feed into pricing, arrival estimates, fleet allocation and delivery promises. This is especially true in Southeast Asian cities, where congestion patterns can be uneven, informal pickup points are common, and road conditions can change quickly.

Singapore is a comparatively orderly market, but it is also demanding. Consumers expect accuracy, regulators scrutinise transport operators closely, and competitors are only a tap away. In that environment, the quality of the underlying location data can shape the user experience as much as the app interface itself.

Also Read: Ryde CEO: ‘NFTs offer greater operational efficiencies in administering a membership programme than traditional systems’

For Ryde, integrating HERE’s technology is unlikely to transform its market position overnight. But it addresses one of the most important questions in ride-hailing: can the platform make better decisions, faster, before the rider even gets into the car?

If it can, the payoff may be felt not in a flashy new feature, but in something more valuable: fewer missed expectations.

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For years, stablecoins were treated mainly as plumbing for crypto trading: a way for traders to move quickly between digital assets without returning to traditional money. That role is now expanding. As more companies, freelancers and cross-border teams look for faster ways to move money, stablecoins are beginning to look less like a crypto niche and more like an alternative payments rail.

Morph, a blockchain infrastructure company focused on stablecoin payments and onchain finance, is the latest firm trying to build for that shift. The company has launched Morph Payments, a non-custodial platform that allows businesses, digital professionals and distributed teams to accept, send and manage stablecoin payments.

Also Read: How stablecoins are quietly reinventing the global dollar system

The first version supports payments in USDC and USDT, two of the world’s most widely used dollar-linked stablecoins. Users can connect a self-custodial wallet, create invoices and payment links, monitor transactions from a dashboard and receive settlement directly onchain. Morph said the platform does not take custody of customer funds.

That point matters. In crypto, custody is not a technical footnote; it defines who controls the money. With Morph Payments, funds are settled directly into the user’s wallet, rather than being deposited with Morph or held by an intermediary before being released.

Stablecoins move beyond trading

The launch comes as stablecoins are gaining wider attention as a tool for commerce, treasury management and cross-border payments. According to Visa’s onchain analytics, adjusted stablecoin transaction volume reached US$10.2 trillion over the past 12 months, up 65 per cent year-on-year.

That figure should be read with care. Stablecoin transaction volumes can include activity across trading, decentralised finance and automated onchain movements, not only payments for goods and services. But the broader direction is clear: stablecoins are no longer used only by traders moving between exchanges. Businesses are testing them for faster settlement, lower-cost international transfers and access to dollar-denominated value in markets where banking rails can be slow or expensive.

This is especially relevant in Southeast Asia, where cross-border commerce is part of everyday business. Freelancers work for overseas clients, e-commerce sellers buy and sell across markets, and startups increasingly hire remote teams across the region. Yet payments often remain fragmented. Bank transfers can take days, fees can be opaque, and smaller businesses may struggle with account access, foreign exchange costs or delayed settlement.

Stablecoins are not a complete answer to those issues. Businesses still face regulatory uncertainty, accounting questions, tax obligations and the practical challenge of converting digital assets into local currency. But for some users, especially those already operating online and across borders, they offer a faster rail for receiving and moving money.

What Morph Payments does

Morph Payments is designed for online businesses, digital freelancers and globally distributed organisations. At launch, it allows users to accept payments in USDC and USDT from customers globally, connect their wallet without depositing funds onto the platform, monitor payment activity through a single dashboard, receive direct onchain settlement at any time, and generate invoices and payment links that lead customers to checkout.

The product is positioned less as a consumer crypto wallet and more as a business payments layer. That means the user experience matters as much as the blockchain infrastructure underneath. Many small businesses do not want to manage wallet addresses, token standards and transaction records manually. They want invoices, payment tracking and a clearer view of what has come in and what has gone out.

“Every major shift in commerce has required new financial infrastructure,” said Renna Ba, Head of Ecosystem at Morph. “As stablecoins become an increasingly important way for businesses to move money globally, payment experiences need to evolve alongside them.”

Ba added that businesses are likely to operate across multiple stablecoins in the same way they operate across multiple currencies today. “The challenge isn’t creating more payment options; it’s making that complexity invisible so businesses can focus on growing, not managing payments.”

Also Read: Stablecoins surge in Southeast Asia 2026: A real shift or just a bridge to CBDCs?

That framing reflects one of the main hurdles for stablecoin adoption. The technology may promise faster settlement, but businesses will not adopt it widely if every transaction requires specialist knowledge. The companies that can hide the complexity while preserving control over funds may have a better chance of moving stablecoins into mainstream commercial use.

The appeal and limits of non-custodial payments

Morph is leaning heavily on the non-custodial nature of the product. Unlike traditional payment processors, which typically receive, process and settle funds into a merchant account, Morph Payments lets businesses connect their own wallet and receive payments directly.

For users, this can reduce counterparty risk. There is no need to wait for a platform to release funds, and no single service provider is holding the customer’s assets. Funds are available once settled onchain, which can improve cash flow for freelancers and small businesses that depend on timely payments.

The trade-off is that self-custody also places more responsibility on the user. If a business controls its own wallet, it must manage private keys, internal controls and security practices properly. Losing wallet access or sending funds to the wrong address can be costly. In traditional finance, mistakes may be reversible. Onchain, they often are not.

This makes education, wallet design and operational safeguards critical. For stablecoin payments to work for mainstream users, platforms need to make self-custody safer and less intimidating without quietly recreating the same custodial risks they claim to avoid.

Why Southeast Asia is a natural testing ground

Southeast Asia has many of the conditions that make stablecoin payments attractive. The region has a young digital workforce, high mobile internet usage, a large creator and freelancer economy, and many small businesses selling across borders. It also has uneven banking access and fragmented payment systems across markets.

A Singapore-based startup may pay contractors in the Philippines, Indonesia or Vietnam. An online designer in Malaysia may work for clients in the US or Europe. A merchant in Thailand may source goods from one country and sell to customers in another. In these cases, payments are not just an administrative step; they affect working capital and day-to-day planning.

Stablecoins can, in theory, make those flows faster. A freelancer who receives USDC or USDT may not have to wait several business days for an international transfer. A business may be able to manage incoming funds around the clock instead of depending on banking hours. For startups with distributed teams, stablecoins may also simplify payments across markets where local banking rails differ sharply.

Still, adoption in Southeast Asia will depend on regulation. Authorities across the region are taking different approaches to digital assets. Singapore has built a relatively mature framework for digital payment token services and stablecoin regulation, while other markets are still clarifying how such instruments should be treated. Any payments platform operating in this space will need to navigate compliance carefully if it wants to serve businesses beyond crypto-native users.

A competitive and fast-changing field

Morph enters a crowded market. Globally, stablecoin payments and crypto checkout are being tackled by companies such as Stripe, which has re-entered crypto payments through stablecoin products; Coinbase Commerce; Request Finance, which focuses on crypto invoicing and payroll; and Triple-A, a Singapore-based licensed crypto payments company. Traditional payments firms are also moving closer to the space, with Visa and Mastercard supporting stablecoin-related settlement and infrastructure initiatives.

In Southeast Asia, the competitive question is not only who can process stablecoin payments, but who can connect them cleanly with compliance, accounting, local currency conversion and business workflows. Many merchants do not want to hold digital assets indefinitely. They may want stablecoin settlement for speed, but still need fiat off-ramps, tax records and integration with existing finance tools.

Morph’s advantage, if it can build it, may come from linking payments to its wider network. The company said the launch “closes the loop” for its ecosystem, allowing customers to use payments received through Morph Payments on trading platforms and yield strategies built on Morph’s network. Morph operates around two networks: a Layer 2 network for stablecoin payments, and Morph Tachyon, a Layer 1 network designed for trading applications and onchain markets.

Also Read: How SMEs are using stablecoins to beat currency swings

That ecosystem approach could appeal to users already comfortable with onchain finance. The bigger challenge is whether Morph can also win over ordinary digital businesses that want the benefits of stablecoins without feeling like they have entered the crypto industry.

The company said more capabilities will be introduced in the coming months. For now, Morph Payments is an early bet on a simple idea: if stablecoins are becoming a financial rail for global commerce, businesses will need tools that make them usable, trackable and less risky.

The stablecoin economy is accelerating. The question is whether products like Morph Payments can make it practical for the businesses outside crypto that move money every day.

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When governments first began talking about regulating social media, the debate revolved around familiar tensions: innovation versus regulation, free speech versus public safety, and economic growth versus platform accountability.

That debate has now entered a different phase.

Across Australia, the European Union and the United Kingdom, child protection has emerged as the political frame through which digital regulation is increasingly being viewed. When online safety becomes primarily about protecting children, governments face far fewer political obstacles to intervening in the digital economy. The question is no longer whether regulation is necessary, but how it should be implemented.

For Singapore, that distinction matters.

The Republic is unlikely to copy Australia’s ban on social media for under-16s or simply import European rules. Our approach to technology governance has traditionally been more pragmatic: encourage innovation while placing clear responsibilities on platforms to manage risk. But the direction of travel is unmistakable. Child safety is becoming one of the defining tests of whether digital platforms deserve public trust.

Recent developments suggest Singapore is already preparing for this new reality. The Online Safety Commission began operations in June, providing victims with a dedicated avenue to seek relief from online harms, while strengthening accountability across the digital ecosystem.

At the same time, the Infocomm Media Development Authority (IMDA) has expanded its online safety regime, requiring stronger age-assurance measures, publishing assessments of major platforms and taking enforcement action where companies have failed to adequately protect users from harmful content.

These are not isolated policy announcements. They reflect a broader shift in regulatory philosophy that businesses should pay close attention to.

History suggests that once an issue is framed around child protection, it rarely moves backwards. Seatbelt laws, restrictions on tobacco advertising and tighter rules around vaping all followed a similar trajectory.

Initial debates focused on individual responsibility and commercial freedom before gradually evolving into questions about implementation and enforcement. Few today would seriously argue that protecting children should take second place to commercial interests.

Also Read: Securing Agentic AI for Singapore enterprises: A reference architecture

Social media appears to be reaching a similar inflection point.

That does not necessarily mean every proposal will prove effective. Australia’s legislation has already prompted difficult questions about age verification, privacy, enforcement and whether determined teenagers will simply circumvent restrictions using VPNs or alternative platforms. Europe is wrestling with similar implementation challenges as regulators seek to balance stronger protections with fundamental rights.

These experiences offer an important lesson for Singapore.

The most effective regulation may ultimately have less to do with restricting access than redesigning digital services themselves.

Around the world, policymakers are increasingly asking whether recommendation algorithms, infinite scrolling, autoplay functions, notification systems and AI-driven engagement tools should be designed differently for younger users. The focus is shifting from content moderation towards product architecture – from policing harmful posts to questioning whether platforms should be engineered to maximise engagement among children in the first place.

That is a more profound change than age verification alone.

For businesses, it signals that ‘safety by design’ could become the next competitive expectation. Companies may increasingly be expected to demonstrate that their products, services and digital experiences have considered children’s wellbeing from the outset, rather than relying solely on parental controls or post-hoc moderation.

Singapore’s regulatory model positions it well for this transition.

Rather than relying on sweeping prohibitions, policymakers have sought to raise standards across the digital ecosystem. IMDA has progressively introduced obligations on social media platforms and app stores, including age-assurance measures designed to reduce children’s exposure to inappropriate content.

Also Read: New Singapore payments code takes aim at hidden mark-ups and misleading “zero fee” claims

More recently, it placed platforms including TikTok and X under enhanced supervision after identifying weaknesses in their ability to detect and remove harmful content, signalling a willingness to hold platforms accountable for outcomes rather than simply prescribing rules.

This reflects an important philosophy. Regulation should go beyond punishing bad behaviour after harm occurs; it should encourage platforms to build safer systems in the first place.

For Singapore’s business community, the implications extend well beyond technology companies.

Consumer brands increasingly market through digital platforms that may face tighter restrictions on advertising or engagement with younger audiences. Financial institutions, healthcare providers and retailers are embedding AI-powered digital experiences into customer journeys. Media companies are rethinking how audiences discover content. All will operate in an environment where public trust and responsible design carry greater commercial value.

Corporate affairs leaders should also recognise that child safety is becoming a reputational issue, not merely a compliance exercise. Investors are placing greater emphasis on governance and responsible technology, while customers increasingly expect companies to demonstrate that digital innovation does not come at the expense of vulnerable users.

The businesses that adapt early are likely to find themselves better placed than those waiting for legislation to dictate change.

Singapore has built a reputation for anticipating global regulatory trends rather than reacting to them. From AI governance to cybersecurity and digital trust, the country has consistently sought to create frameworks that support innovation while maintaining public confidence.

Online child safety presents another opportunity to demonstrate that balance.

Rather than asking whether Singapore should follow Britain or Australia, policymakers and business leaders should focus on the larger question those countries have raised: what does responsible digital innovation look like when child protection becomes a central measure of success?

The answer is unlikely to lie in blanket bans or laissez-faire regulation. It will require governments, platforms and businesses to accept that protecting children is no longer a peripheral policy objective but a core expectation of the digital economy.

The global debate over whether governments should intervene has largely run its course. The harder task now is building digital environments that are safe by design, commercially sustainable and trusted by the communities they serve.

Singapore has an opportunity not simply to follow that conversation, but to help define what comes next.

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For most photographers watching generative AI learn to render skin, fabric and light with unsettling accuracy, the instinct has been to defend the craft.

Vincent Chow, however, did the opposite. The founder of Singapore-based product photography firm SnappyFly decided that if AI was coming for his industry, he would rather be the one driving it in than be run over by it.

Founded in 2018, SnappyFly is a technology-driven product photography company specialising in automated product photography, high-volume e-commerce imaging and AI-powered commercial content production. Through automation, proprietary software and AI, SnappyFly helps businesses create high-quality visual content faster, more efficiently and at enterprise scale.

Also Read: The scarcity mindset is killing creativity, not AI

The startup built its reputation on automating the unglamorous mechanics of product photography for e-commerce brands — the repetitive shoots, the catalogue images, the high-volume work that retailers need but rarely think about. Now the company has launched SnappyFly.ai, a platform that turns a single photoshoot into a full marketing campaign — lifestyle shots, AI-generated models, outdoor scenes and social content, all produced from images captured in-studio.

It is a pivot built on a philosophy Chow repeats often — that technology is not something to be fought. But that framing raises an obvious question: is this genuine conviction, or simply the most convenient story available to a founder who had no real alternative?

The threat was never theoretical

Chow does not dodge the question. “Yes, I actually think AI was a threat and I still think it is today,” he said. “I don’t think threat and opportunity are mutually exclusive.”

He has been in the creative industry for more than two decades, long enough to have heard every argument for why AI could never replace professional photographers: the outputs weren’t accurate enough, the people looked artificial. Chow never found that reassuring. “These were descriptions of AI’s limitations at that moment, not permanent limitations. Technology will improve as it always does.”

Also Read: Magnific bets on human‑led AI infra for marketing and film work

That belief was tested directly. SnappyFly’s early experiments involved using Gemini to place professionally photographed clothing onto AI-generated models. The client noticed immediately that the products didn’t look right and the models didn’t look real. Nothing commercial was lost; it was experimental work. However, it exposed the gap between AI that looks impressive and AI that a brand can actually put its name behind.

The client who wouldn’t pay

That gap produced the moment Chow now describes as the company’s real turning point. Six months after the failed Gemini attempt, SnappyFly tried again with a client, using improved AI tools. This time the images were convincing. The client was impressed and then asked why they should pay for something anyone could generate with freely available consumer tools.

It’s a problem that doesn’t go away with better technology; if anything, it gets sharper. Asked how SnappyFly stops enterprise clients from asking the same question again once today’s techniques are commoditised, Chow doesn’t pretend there’s a permanent fix. “We don’t,” he said. “Technology advances, and that is exactly why we are able to develop our current AI platform when we couldn’t do the same 12 months ago.”

He points to SnappyFly’s own history as the model. When the company first automated its photography workflow in 2018, competitors caught up within 18 to 24 months. The response wasn’t to defend that advantage; it was to find the next one. “Our job isn’t to protect yesterday’s advantage. It’s to keep creating tomorrow’s one.”

Orchestration isn’t the moat; knowing what to orchestrate is

SnappyFly.ai is built on existing AI models rather than proprietary foundational technology, which invites a fair challenge: what stops a well-funded competitor, or a client’s own in-house team, from rebuilding it within a year?

Chow’s answer isn’t that it’s impossible; it’s that it may not be worth doing. “Absolutely, [it can be replicated]. This is the case with most technology-enabled businesses,” he said. The platform’s value, he argues, sits in the workflows and methodologies layered around the models: how shooting techniques and generation techniques are matched to preserve product accuracy, how the automated photography machines feed directly into the AI pipeline. “Our enterprise clients won’t necessarily want to become experts in AI image production. They want commercially usable content.”

Also Read: Thriving in the age of AI: What the media industry must do next

Even that defence has an expiry date, and Chow doesn’t dispute it. Asked what happens the day OpenAI, Google or Adobe ships a native tool solving the exact commercial-accuracy problem SnappyFly was built around, his answer is unusually blunt for a founder discussing his own moat: “Truthfully speaking, we would celebrate it.” The company, he says, is model-agnostic by design, and would simply move to the next unsolved problem.

Betting against his own revenue

Perhaps the sharpest tension in SnappyFly’s pitch is financial. If one shoot can now generate an entire campaign’s worth of assets, that implies fewer reshoots, fewer studio sessions and less billable time, the opposite of what a photography business wants.

Chow accepts the trade-off rather than argues around it. “A customer may need fewer physical shoots, but from each shoot we can potentially help them produce far more usable content,” he said. It means SnappyFly can produce catalogue images, lifestyle visuals, model shots and marketing assets from a single session. “There is obviously some cannibalisation involved here at the initial stage. I’m perfectly comfortable with that. I would rather SnappyFly be the one that champions this technology and cannibalise part of our own traditional photography business than wait for somebody else to do it for us.”

No layoffs, but no illusions either

Creative industries have been among the most publicly anxious about AI displacement, and Chow’s own photographers and designers were not exempt from that unease. He says the pivot has led to role redesigns but no layoffs, largely because the team was brought into the process early, meaning they were shown the tools, given access to experiment, and invited to suggest applications for their own work. “It was never about trimming manpower,” he said. “It was about finding what can set us apart when we use AI for clients.”

The legal grey zone nobody can solve yet

SnappyFly has also built a Responsible AI Framework covering copyright and IP checks, but Chow is careful not to oversell it in a legal environment that remains unsettled globally. “We cannot guarantee or tell a client that our framework… can eliminate legal risks and uncertainties. And I feel it would be irresponsible for us to claim that,” he said. What the framework offers instead is process: human review, documentation, and transparency with clients about how AI was used, reducing avoidable risk rather than promising there is none.

Also Read: How creativity, commerce and AI collide in mid-2026 marketing mix

It’s a fitting note for a company whose entire strategy rests on discomfort with certainty. Chow isn’t claiming SnappyFly has built something un-copyable, or a legal shield that guarantees safety. His bet is narrower and arguably harder to sustain: that the company can keep finding the next problem before someone else does, indefinitely.

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