Enterprise AI is entering a more dangerous phase, one where confidence is high, but control is dangerously low. A new report by Gravitee highlights a growing disconnect: while 82% of executives believe their AI security policies are robust, less than half of AI agents are actually monitored or secured.

This gap is especially concerning in Southeast Asia, where regulatory frameworks are fragmented and evolving. Companies operating across multiple ASEAN markets must navigate overlapping privacy, cybersecurity, and sector-specific rules; yet many still confuse compliance with actual control.

The real risk is not just flawed outputs, but unauthorised actions. As AI agents gain the ability to interact with systems, access data, and execute workflows, governance failures shift from theoretical to operational. A misstep is no longer a bad answer; it could mean data breaches, compliance violations, or financial exposure.

Compounding the issue, budgets for AI security are stagnating, signalling that many organisations are relying on policy rather than infrastructure. But regulation is unlikely to arrive neatly. Enforcement will come through audits, incidents, and customer demands.

For startups and enterprises alike, the message is clear: governance is no longer a checkbox. It is the foundation for scaling AI safely and competitively in a region where complexity is the norm.

Regional

SEA ecommerce hits US$157.6B GMV in 2025, up 22.8%: Shopee, Lazada, and TikTok Shop collectively held 98.8% of platform gross merchandise value, with Thailand and Malaysia recording the fastest growth at 51.8% and 47.6% respectively, per Momentum Works.

Temasek CEO takes helm at Vertex Venture Holdings: Dilhan Pillay succeeds Teo Ming Kian as chairman effective April 15, as assets under management grew from US$200M to US$7B under the outgoing chairman’s tenure since 2012.

OKX Ventures and HashKey back Vietnam’s regulated crypto exchange: The newly formed CAEX has raised its capital base to US$380M, meeting the threshold for Hanoi’s pilot programme. Vietnamese users moved around US$200B in digital assets through mid-2025, mostly via offshore venues.

Philippines orders Meta to act on disinformation within 7 days: Authorities warned that fake documents about President Marcos Jr. and misleading military and financial content violate the country’s penal code and cybercrime laws, threatening public order and national security.

OrtCloud bags US$1.7M pre-seed to fix cloud chaos for AI: Backed by Golden Gate Ventures and Antler, the Singapore-based startup offers fixed-resource virtual machines with deterministic performance, targeting a US$20B+ AI workload market in Southeast Asia.

GSM launches EV driver platform in Indonesia, Philippines: VinFast EV owners and renters can sign up as driver partners, keeping up to 90% of revenue. Free charging is offered until March 2029, with registration and insurance support added in the Philippines.

South Korea rolls out AI smart city pilots across SEA: Six projects under the 2026 K-City Network programme span Brunei, the Philippines, Vietnam, Thailand, and Malaysia, covering traffic, transport, disaster response, and building safety applications.

Singapore trials robotaxis in Punggol with Chinese AV firms: The government targets 100 to 150 autonomous vehicles by year-end, with Grab and ComfortDelGro cleared to partner WeRide and Pony.ai as Chinese AV firms push overseas expansion.

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Interviews & Features

The expensive middle: what career transitions really cost: A former Singapore Sports School GM spent three and a half years delivering food, crashing a motorcycle, and exhausting his savings before passing his real estate licence in 2022, exposing the hidden costs of career change that go beyond motivation.

Why financial and legal literacy is a founder’s survival skill: SEA founders routinely walk into five avoidable pitfalls, from messy cap tables and contractor misclassification to signing term sheets without understanding liquidation preferences, that only surface at the worst possible moments.

When north star metrics start narrowing a company’s vision: Scaling across Asia’s uneven markets, leaders often oversimplify dashboards until local signals stop surfacing and clean numbers mask an outdated picture of reality, the real risk isn’t bad data, it’s frozen judgment.

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International

Zepto trims cash burn ahead of planned US$1.2B IPO: The Bengaluru-based quick commerce startup has confidentially filed for an IPO and is pitching profitability by FY29 to institutional investors, with quarterly EBITDA losses narrowed to roughly US$6M.

Meta set to surpass Google in global digital ad revenue in 2026: Emarketer projects Meta’s net ad revenue at US$243.46B versus Google’s US$239.54B, driven by 24.1% growth fuelled by WhatsApp, Threads, and Instagram Reels.

Anthropic hires Trump-linked lobbying firm after Pentagon clash: Ballard Partners was engaged days after the Pentagon designated Anthropic a supply chain risk. The AI firm spent US$3.1M on federal lobbying in 2025, up more than 330% year-on-year, as talks over government access to its tools broke down.

Crypto wallet firm Exodus sues W3C to close US$175M deal: Exodus alleges W3C and its CEO tried to avoid completing the acquisition of crypto card firms Baanx and Monovate, despite US$80M in loans already disbursed, including US$10M to the CEO personally.

Trump-linked crypto project faces investor revolt over token controls: Billionaire backer Justin Sun accused World Liberty Financial of secretly building controls to freeze token holders’ funds, as the firm also faces scrutiny over a US$75M stablecoin loan collateralised by its own tokens.

Japan’s SoftBank, NEC, and Honda form physical AI venture: The joint venture will develop AI for robots and vehicles, backed by Japan’s government plan to invest 1T yen (US$6.27B) in domestic AI projects over five years as Tokyo bids to close the gap with the US and China.

Chinese EV makers race to build in-house chips as rivalry deepens: Nio and Horizon Robotics are both launching proprietary intelligent driving chips this year as competition shifts from output volume to high-value technology, with semiconductor supply chain concerns escalating across the industry.

Bitcoin’s US$74K surge: institutional conviction or macro mirage?: Bitcoin climbed 5.38% to US$74,532, driven by spot ETF inflows of US$1.1B for the week, including US$612M into BlackRock’s iShares trust in a single day; yet a 94.5% correlation with the S&P 500 raises questions about its role as a hedge.

Crypto falls in lockstep with equities as geopolitical tensions rise: The crypto market fell 1.17% to US$2.42T after the collapse of US-Iran talks triggered a broad risk-off selloff, with digital assets showing a 94% correlation with the S&P 500 and 88% with gold.

Indian AI startups tackle science, engineering, and GPU reliability: Firms including ZeneteiQ, Oru’el, and HumanTronik are moving beyond app-layer products to build core scientific and physics-based models, though talent shortages and limited test environments remain key gaps.

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Cybersecurity

FBI and Indonesian police dismantle W3LL phishing marketplace: The operation targeted over 17,000 victims globally, selling kits for US$500 that enabled over US$20M in attempted fraud by generating fake login pages to steal passwords and multi-factor authentication codes.

Booking.com confirms unauthorised access to customer data: Personal details including names, phone numbers, addresses, and booking information may have been accessed by third parties, with at least one customer reporting a targeted WhatsApp phishing message containing their booking details.

Anthropic’s Mythos AI triggers cybersecurity alarm in India: The model’s ability to find software flaws in hours has prompted HDFC Bank and others to reassess exposure, with experts warning that legacy-dependent banks and telecoms are most vulnerable under India’s slower-paced cyber regulations.

SEA’s AI blind spot: executives overconfident, agents unmonitored: Gravitee research found that while 82% of executives report high confidence in AI security policies, only 47.1% of AI agents are actually monitored or secured — a dangerous gap in a region with fragmented regulatory frameworks.

How modern money laundering hides inside tech startups: The collapse of Builder.ai, a US$1.5B AI unicorn that used 700 engineers to fake automation, illustrates how inflated startup valuations and round-tripping schemes can serve as facades for financial crime, especially in loosely regulated innovation hubs.

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Semiconductor

Chinese EV chipmakers accelerate as supply chain fears mount: Nio’s in-house driving chips and Horizon Robotics’ upcoming cockpit chip signal that China’s EV sector is investing upstream into semiconductors, while global carmakers like Volkswagen deepen reliance on Chinese suppliers including CATL and Xpeng.

Nvidia denies report of talks to acquire Dell or HP: Shares of Dell jumped 6.3% and HP rose 2.3% after a SemiAccurate report claimed Nvidia had been in talks to reshape the PC market through a major acquisition, which Nvidia flatly denied.

Nvidia acquisition rumour briefly lifts Dell and HP shares: A now-denied report of Nvidia pursuing a major PC maker acquisition briefly moved markets, underscoring investor sensitivity to consolidation signals in the semiconductor and hardware supply chain. Micron dipped 2.12% in the same session, signalling persistent unease.

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AI

Singapore’s AI ambition outpaces its governance foundations: PwC’s Global AI Survey 2026 found 67% of Singapore businesses have a higher appetite for AI risk than the global average of 41%, yet only 47% have documented responsible AI frameworks and 37% have redesigned workflows to genuinely embed AI.

Without AI governance, agents become enterprise chaos engines: Gravitee’s report argues that enterprises need a unified AI identity and governance layer built around visibility, scoped access, and runtime policy, with 73% of CISOs citing API identity discovery as their top investment priority.

Why AI agents need centralised control, not just policies: Agentic AI introduces a new risk category, unauthorised operational execution, that differs fundamentally from chatbot errors. For SEA businesses operating shared-service models across multiple jurisdictions, a single ungoverned workflow can cascade across systems and borders.

The right to AI explainability runs into a technical wall: Regulators increasingly expect decision traceability from AI systems, but foundation models generate outputs through probabilistic processes that their own builders cannot fully interpret, making system-level explainability, not model-level, the realistic compliance target.

South Korea deploys AI smart city tech across five SEA nations: The 2026 K-City Network selected six pilot projects spanning water management, traffic control, transport, and building safety, with Korean firms positioned to expand regionally through the initiative.

India’s AI model-builders go deep into science and engineering: Companies backed by the IndiaAI mission are developing physics-based, scientific, and GPU reliability models rather than consumer-facing apps, though founders cite engineer shortages and limited compute environments as ongoing constraints.

Japan bets US$6.27B on physical AI for robots and vehicles: A new joint venture by SoftBank, NEC, and Honda will develop physical AI for robotics and automotive applications, underpinned by a government funding commitment of 1T yen over five years aimed at narrowing Japan’s gap with the US and China.

AEO: the AI search strategy traditional industries are missing: Answer Engine Optimisation restructures content so AI systems cite it directly. Manufacturing, logistics, and legal sectors face a 20% year-on-year traffic drop from AI-powered search, yet fewer than 10% of sources cited by ChatGPT and Gemini rank in Google’s top 10.

If AI is changing everything, why does nothing look different yet?: AI’s labour market impact follows an S-curve — beginning with reduced junior hiring rather than mass layoffs, before reaching a Threshold Substitution point that forces competitive restructuring. Entry-level tech job postings have already plunged 50% from pre-pandemic levels.​

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Thought Leadership

Risk management is Southeast Asia’s secret 2026 growth engine: Beyond resilience, the highest-performing enterprises are becoming antifragile, using integrated Enterprise Risk Management to turn systemic volatility into a competitive advantage, unlock cheaper capital, and build a compliance moat against rivals still working manually.

How tiny daily habits secretly compound into company-defining wins: Breakthroughs rarely emerge from single events. Leaders who build organisational trust capital and cognitive momentum through small, consistent daily choices, gratitude notes, learning rituals, generosity defaults, compound advantages their rivals cannot easily replicate.

Governance without friction: the only AI control model that works: Security controls only work if they are easier to use than the unsafe alternative. Enterprises that make compliant AI adoption the default path,  through short-lived tokens, runtime policy enforcement, and audit trails, will outpace those still relying on governance theatre.

SEA founders must stop treating legal and finance as afterthoughts: Across post-mortems in the region, the most common thread is a financial or legal decision made early and casually that compounded into something irreversible, from SAFE misunderstandings to revenue recognition errors that sent investors walking.

Career transitions cost more than anyone admits. Here’s why: The sanitised narrative of clean career pivots ignores identity loss, decision fatigue, and relationship strain. Working parents face compounding pressures that solo founders never encounter, making the “fail fast” ethos genuinely dangerous in some contexts.

When metrics become referees, Asian companies stop seeing clearly: In markets as uneven as Asia’s, dashboards that once enabled growth can quietly suppress inconvenient local signals until the assumptions baked into the numbers no longer match the market they were built to measure.

The post Ecosystem Roundup: Confidence theatre meets AI reality appeared first on e27.

Enterprise AI has reached the point where hand-wringing is no longer enough. The urgent question is practical: what should organisations actually build if they want autonomous agents without autonomous chaos?

The “AI Agent Governance Gap” report by US-based API management company Gravitee offers a clear answer. It argues that the future lies in a unified AI identity and governance layer built around visibility, scoped access, runtime policy, and comprehensive observability.

Also Read: AI agents are already inside your systems, but who’s controlling them?

That may sound like vendor language, but the underlying logic is hard to dispute. If AI agents are going to interact with large language models, APIs, databases, internal tools and emerging agent protocols, such as MCP, then those interactions need a control plane. Otherwise, enterprises will continue managing twenty-first-century automation with twentieth-century access assumptions and hoping luck remains employed.

The report says the three immediate priorities are inventory and visibility, governance primitives, and unified authorisation. Some 73 per cent of CISOs said API and workload identity discovery would be their top area of investment if budget were not a constraint. Another 68 per cent prioritised continuous monitoring and posture analytics. These are not cosmetic upgrades. They are the plumbing of governable AI.

Why the gateway is back in fashion

For years, API gateways were often discussed as middleware: useful, necessary, not especially glamorous. AI changes that. Once organisations connect internal agents to external models and internal systems, the gateway becomes the natural chokepoint where policy can actually be enforced.

Also Read: It’s not the chatbot but the access: Why AI agents are the real threat

Gravitee’s white paper makes this case directly. Instead of allowing agents to integrate independently with providers such as OpenAI, Bedrock, or Gemini, enterprises can proxy access through a central control point. That creates immediate benefits: authentication and authorisation can be standardised, token consumption can be monitored and limited, content can be inspected for sensitive data or prompt injection, and usage can be observed across providers in one place.

For Southeast Asia, this matters for three reasons.

First, cost discipline. Many regional startups and enterprises are enthusiastic about AI but deeply sensitive to runaway inference bills. Token-based rate limiting and usage observability are not just security features. They are financial controls.

Second, vendor flexibility. Companies across the region are increasingly wary of lock-in, especially as they balance global foundation models against local hosting, private deployments and open-source alternatives. A gateway layer makes it easier to switch, route or combine providers without rewriting every downstream integration.

Third, compliance. Centralising traffic makes it easier to apply rules about data handling, retention and model access. That is particularly useful for organisations operating across ASEAN markets with different expectations around privacy and sensitive data.

MCP and agent-to-agent traffic will need their own guardrails

One of the more forward-looking parts of the report concerns MCP, the emerging protocol layer that allows AI agents to discover and invoke tools in a more standardised way. Gravitee argues that enterprises should not treat MCP as a collection of point-to-point connections. They should govern it centrally.

Also Read: The hidden risk in AI adoption: Unchecked agent privileges

That is a shrewd observation. The moment agents can discover capabilities dynamically, the old idea of static approved integrations starts to weaken. Security teams need to know which tools an agent can see, which prompts or methods it can invoke, which resources it can access and whether those permissions still make sense.

In practical terms, the report envisions protocol-aware proxying, a central registry of deployed AI agents, compliance with MCP authorisation flows and granular access policies controlling tool discovery and invocation. In less formal language: do not let agents wander the digital office unsupervised.

This is especially relevant in Southeast Asia because many businesses are trying to move fast with relatively lean teams. A standard way to expose internal capabilities to agents is attractive. But standardisation without governance simply scales mistakes more efficiently.

The winning model is governance without friction

Perhaps the report’s most commercially important insight is that security controls only work if they are easier to use than the unsafe alternative. This is the antidote to shadow AI. If developers and business teams can access approved models, tools and APIs quickly through a governed layer, they are less likely to bypass it.

That principle should resonate across Southeast Asia’s tech scene. The region’s best companies rarely succeed by saying “no” more loudly. They succeed by building faster, smoother systems that align business speed with operational discipline. AI governance will be no different.

A useful mental model is this: the goal is not to slow down agent adoption. The goal is to make compliant adoption the default path. That means provisioning agents with clear ownership, issuing short-lived tokens bound to specific resources, enforcing contextual policy at runtime and maintaining audit trails that can withstand customer scrutiny, regulator questions and incident response.

Also Read: Southeast Asia’s AI blind spot is getting bigger

For founders and product leaders, that may feel like heavy infrastructure. In practice, it is enabling infrastructure. Companies that solve this layer early will be able to deploy AI into revenue-generating and regulated workflows with far greater confidence.

The post Without governance, AI agents risk becoming enterprise chaos engines appeared first on e27.

Singapore-based growth financing platform Choco Up has launched a US$30 million private credit facility in partnership with CHUAN, a tech-driven credit specialist focused on the digital economy, to put faster, more reliable working capital in the hands of SMEs across Asia Pacific.

The first drawdown has already been completed, a signal that, at least for now, the market appetite is real.

Also Read: Understanding private credit: Filling the gaps left by banks

The funding gap nobody is solving fast enough

For all the noise about Asia’s startup boom and venture capital frenzy, the region’s backbone — its tens of millions of SMEs — remains chronically underfunded. Banks demand collateral, long operating histories, and months of paperwork. The result is a structural mismatch between when SMEs need money and when they can actually get it.

This is not simply a capital availability problem. It is a timing problem. A manufacturer that has shipped goods but is waiting 90 days for payment cannot afford to wait six months for a bank loan to be approved. A digital commerce seller facing a seasonal demand spike needs funding decisions measured in hours, not quarters.

At the same time, investor appetite has historically skewed towards startups rather than SMEs, and with good reason, from a returns perspective. Startups offer the possibility of exponential growth, equity upside, and portfolio-defining outcomes. SMEs, by contrast, tend to grow linearly, generate steady but unspectacular returns, and offer little of the asymmetric payoff that venture investors seek.

The result is a two-tier capital market where high-risk, high-reward bets attract institutional attention while profitable, established small businesses are left to scrape together funding from overdrafts, trade credit, government grants, and family networks.

Private credit is increasingly positioned as the answer to this structural gap, but understanding what it actually offers and where it falls short matters.

What private credit can and cannot do for SMEs

Private credit refers to lending provided outside of traditional banking and public debt markets, typically from institutional investors such as asset managers, family offices, credit funds, and insurance companies. For SMEs, it can offer a meaningful alternative when banks won’t lend quickly enough, or at all.

The advantages are real. Private credit facilities can move significantly faster than conventional bank loans, with approval timelines collapsing from months to days or even hours when real-time data powers underwriting. Facilities are often structured with greater flexibility than rigid bank products, with repayment tied to business performance rather than fixed schedules.

Also Read: Choco Up, Wonder Capital join forces to launch US$50M private credit funds for APAC SMEs

Crucially, unlike equity financing, private credit does not dilute founders’ ownership stakes, a significant consideration for SME owners who have spent years building their businesses and have no interest in giving away a slice of them.

Choco Up’s partnership with CHUAN leans into these strengths. By combining CHUAN’s access to institutional capital with Choco Up’s AI-driven credit assessment, which draws on real-time business performance data, the facility promises funding approvals in as little as a few hours. Capital providers, meanwhile, gain near real-time visibility into the underlying asset performance, a level of transparency that has historically been absent from SME lending.

“SMEs today don’t just need access to capital. They need financing that keeps pace with how their businesses operate,” said Percy Hung, CEO and founder of Choco Up.

But private credit is not without its complications. The cost of capital is typically higher than a bank loan, reflecting the risk premium demanded by non-bank lenders operating in a less regulated space. SMEs that rely too heavily on private credit facilities without a clear path to profitability can find themselves in a cycle of rolling debt at increasingly punishing rates. Transparency on fees and terms can also vary significantly between providers, leaving less sophisticated borrowers exposed.

The governance and oversight frameworks around private credit markets in Asia are also still developing. Unlike bank lending, which is heavily regulated across the region’s major jurisdictions, private credit operates with considerably more latitude, which cuts both ways. For nimble operators, it is a feature. For borrowers who do not fully understand the terms they are signing, it can become a liability.

Private credit versus venture debt: not the same animal

It is worth drawing a clear distinction between private credit and venture debt, two instruments that are sometimes conflated but serve very different purposes.

Venture debt is designed specifically for startups, typically those that have already raised equity funding from venture capital investors. It is structured as a complement to equity rounds, providing additional runway without further dilution. Lenders price venture debt on the assumption that the borrower has VC backing as a credibility signal, and deals often include warrant coverage, the right to buy equity at a fixed price, as additional compensation for the lender’s risk.

Private credit, as deployed through the Choco Up-CHUAN facility, is aimed squarely at operating businesses with real revenue, not high-burn startups chasing growth at any cost. The underwriting is based on demonstrated business performance: cash flows, transaction data, and operational metrics, not the identity of a startup’s investors or the promise of a future funding round. The repayment structure reflects this too, with facilities designed to align with how a business actually generates and collects cash.

For Lin Tun, founding partner and chief investment officer of CHUAN, the institutional opportunity here extends beyond any single market. “This partnership is central to CHUAN’s strategy of curating a network of proven tech partners, providing global investors with access to diversified credit assets with attractive yields that have largely been untapped by the capital markets,” he said.

A platform play with regional ambitions

Choco Up brings more than technology to the table. The company claims to have enabled over US$1 billion in gross merchandise value across its portfolio, giving it a meaningful track record in flexible, equity-free financing across Southeast Asia and beyond. CHUAN provides capital markets infrastructure for aggregating and distributing credit assets at scale, alongside a global investor network.

Also Read: Choco Up, Set Sail AI forge partnership to help businesses grow through Gen AI adoption

The combined pitch to institutional investors is essentially this: SME credit in Asia, structured with the kind of data transparency and underwriting rigour that have historically been reserved for larger corporate borrowers, is now accessible as a diversified, relatively short-duration asset class.

Whether that pitch translates into sustained capital deployment at scale will depend on whether the technology infrastructure can withstand stress, and whether SMEs across the region can access the facility on terms that genuinely help rather than merely substitute one form of financial pressure for another. For Asia’s US$2.5 trillion funding gap, a US$30 million facility is a start. It is a long way from a solution.

The post Choco Up taps US$30M to tackle Asia’s SME funding squeeze appeared first on e27.

Startups amplify everything-uncertainty, pressure, ambiguity. And with that amplification comes something less often discussed: the emotional load of working in these environments. Every decision, every interaction, every outcome feels larger because the margin for error is small, and the stakes are intensely personal. In a corporate setting, failure is often buffered by systems, processes, and teams. In a startup, it lands squarely on your shoulders, often faster than you realise.

I’ve noticed that the moments when my emotions are hardest to manage often align with the moments that test me most as a founder or early employee. A minor disagreement in a meeting, an overlooked task, or a shifting process can trigger frustration that grows faster than logic can keep up. In corporate roles, I had the structure to absorb it; in startups, there is little buffer. That intensity makes emotional self-awareness not just valuable-it’s essential.

Over time, I’ve learned to adopt a simple, practical approach: pause and process. It’s not a perfect system, and it doesn’t eliminate frustration, but it allows me to step back and consider what’s happening before reacting. This habit has helped me navigate three recurring challenges that seem to define the startup experience.

The first is process frustration

Startups are fluid by design. Rules are undefined, priorities shift constantly, and what works one day may be irrelevant the next. Coming from a structured corporate background, this initially felt uncomfortable-almost disorienting. Instead of reacting with immediate frustration, I began asking a different question: What is this environment trying to teach me? That shift from resistance to curiosity has opened more doors than I expected. It allowed me to participate constructively in shaping processes rather than getting caught in a loop of complaint.

Also Read: How tech startups can attract Gen Z and millennials seeking flexibility and purpose

The second is people frustration

Early-stage teams often bring together individuals with widely different backgrounds, experiences, and ways of thinking. Alignment doesn’t come naturally, and miscommunication is inevitable. When tensions arise, I’ve found it helpful to reframe the situation internally: from “Why isn’t this done my way?” to “What might their approach reveal that mine doesn’t?” This doesn’t remove friction, but it transforms it into a productive force, encouraging me to understand rather than resist, to adapt rather than criticise.

The third is outcome frustration

In startups, the consequences of failure are often immediate, visible, and personal. A delayed product release, a missed target, or a misjudged strategy can feel like a reflection of your own capability. In those moments, it’s easy to spiral into self-doubt or overcorrection. Having a space, whether through reflection, journaling, or talking with a trusted sounding board, helps me regain perspective. Even small reframing exercises can make the difference between dwelling on setbacks and taking constructive action.

The common thread across these challenges is that unmanaged emotions don’t just affect you-they ripple out to teams, decisions, and the overall trajectory of the startup. Emotions themselves are not the enemy of professionalism. The real challenge is unprocessed emotions. When we ignore or suppress them, they have a way of leaking into our work, our decisions, and our interactions in ways that can be damaging.

Also Read: Startups, is your email strategy driving growth, or just gathering dust?

The practical takeaway is simple: you don’t need to be emotionless to be effective. In fact, acknowledging and understanding emotions can be a competitive advantage. But we do need mechanisms to process before projecting. Reflection, conversations, and intentional pause create space to make sense of what we feel and why. That space allows us to turn emotions into clarity, empathy, and better decision-making.

In a startup environment, this ability isn’t a luxury-it’s a survival skill. It helps you navigate ambiguity, work better with diverse teams, and maintain perspective when outcomes don’t go as planned. Most importantly, it allows you to stay grounded, remain engaged, and continue growing without being derailed by the intensity that is inevitable in early-stage ventures.

Emotions are amplified in startups, but they don’t have to be destructive. Managed well, they become signals, guides, and even sources of energy. And when you learn to listen, process, and respond thoughtfully, those emotional moments stop being obstacles-they become tools for better work, stronger teams, and longer-lasting engagement.

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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 post Emotions matter more in startups appeared first on e27.

Navigating manufacturing HR challenges in Singapore (2026)

As we move through 2026, manufacturers in Singapore are facing a transformative yet volatile landscape. The primary challenge lies in the acute shortage of specialized technical labor, compounded by stricter foreign workforce quotas and the rising levies associated with the COMPASS framework. Furthermore, the push toward “Industry 4.0” has created a digital divide; many firms struggle to integrate legacy shop-floor machinery with modern data-driven management systems. Rising operational costs—driven by fluctuating energy prices and high land premiums in Singapore—demand unprecedented efficiency. Manufacturers are also under pressure to implement real-time workforce tracking to manage complex shift rotations and ensure compliance with evolving Ministry of Manpower (MOM) safety and welfare regulations in a post-automation era.

Why specialized HRMS trumps conventional software

HRMS software for manufacturers is fundamentally different from standard commercial HR tools because it bridges the gap between administrative personnel management and the physical reality of the factory floor. While standard software treats employees as static entries, a manufacturing-centric HRMS views the workforce as a dynamic component of production capacity.

• Complex Shift & OT Management: Handles 24/7 rotating shifts, overnight patterns, and complex overtime calculations that standard software cannot process.
• Production Linkage: Integrates with shop-floor data to track labor costs per project or production line.
• Skills & Certification Tracking: Automated alerts for expiring safety certifications or specialized machine operating licenses.
• High-Volume Transaction Handling: Designed to process thousands of clocking records daily from various biometric points without latency.

Unique system requirements for Singapore manufacturers

Singapore’s regulatory and geographical context imposes specific demands on HRMS architecture that are rarely found in global “one-size-fits-all” solutions. The integration of localized statutory requirements with Singapore’s specific banking and digital infrastructure is non-negotiable for 2026.

• MOM & CPF Integration: Seamless, automated API hooks for CPF contributions, AIS for tax filing, and foreign worker levy (FWL) calculations.

• Skillspark & Government Grant Tracking: Capability to track training hours and claimable expenses under various Enterprise Singapore (ESG) or WSQ grants.

• Multi-Location Biometrics: Support for geo-fencing and facial recognition across multiple Tuas or Jurong-based facilities, integrated into a single database.

• Public Holiday & Rest Day Logic: Specific handling of Singapore’s Employment Act regarding work on rest days and public holiday substitutions.

Also read: AI agents and ERP: Why Singapore businesses must act now

The hidden cost of “Accounting Package + Customization”

Many manufacturers attempt to save costs by adding HR modules to a general accounting package. In 2026, this approach often leads to “Digital Debt.” General accounting systems lack the granular database schema required for complex manufacturing payroll. Customizing these packages usually results in a “Frankenstein” system that is difficult to upgrade. When the MOM changes a regulation, a customized accounting package requires expensive manual recoding, whereas an industrial-fit HRMS is updated via standard patches. The result of using a generic package is typically a loss of data integrity, inaccurate labor costing, and a high risk of non-compliance fines that far outweigh the initial “savings.”

Top 5 popular HRMS software

Selecting the right Human Resources Management System (HRMS) is critical for maintaining a competitive edge in Singapore’s manufacturing sector. Below are the top five solutions currently leading the market.

1. Multiable

A. Pros

• Seamless integration between payroll and complex manufacturing shift rosters.
• Highly scalable architecture that supports rapid regional expansion.
• Multiable HCM offers advanced AI-driven predictive analytics for manpower planning.
• Full compliance with Singapore MOM, CPF, and IRAS regulations out-of-the-box.
• High level of configurability without requiring core code changes

B. Cons

• Support service in weekend or public holiday will incur extra charge.
• Only suitable for mid-sized or large enterprise. Price may be out of touch for mom-and-pop business.
• Implementation phase requires a dedicated internal project team due to system depth.

C. How the vendor meets the unique requirement

• Features a dedicated Singapore-specific statutory engine for CPF and FWL.
• Built-in module for tracking WSQ training grants and Skillspark integrations.
• Supports high-frequency biometric data sync from multiple factory sites in Singapore.
• Learn more about Multiable HCM

2. SAP SuccessFactors

A. Pros

• Global standard for enterprise-grade human capital management.
• Deep integration with SAP ERP manufacturing modules (PP/MM).
• Robust self-service portal for a diverse, multilingual workforce.

B. Cons

• Long and costly implementation cycles.
• Complex user interface that may require extensive employee training.
• High total cost of ownership including maintenance and consultant fees.
• High resource consumption on local infrastructure.

C. How the vendor meets the unique requirement

• Provides localized payroll clusters specifically for Singapore tax laws.
• Extensive reporting tools for foreign worker quota management.
• Secure cloud hosting options compliant with Singapore’s PDPA.

3. Oracle Cloud HCM

A. Pros

• Strong focus on data security and high-availability architecture.
• Comprehensive talent management and succession planning tools.
• Built-in AI for resume screening and candidate matching.

B. Cons

• Often viewed as too rigid for highly specific local manufacturing workflows.
• Integration with third-party biometric hardware can be challenging.
• Significant learning curve for HR administrators.
• Frequent update cycles can occasionally disrupt custom workflows.

C. How the vendor meets the unique requirement

• Offers a localized Singapore Legislative Data Group (LDG).
• Automated updates for Singapore Budget changes (e.g., CPF rate adjustments).
• Global platform that manages Singapore-based headquarters with regional factory oversight.

Also read: The architect’s mandate: Building a resilient foundation for the intelligent enterprise

4. Workday

A. Pros

• User-friendly, modern interface that encourages high adoption.
• Continuous innovation with frequent, seamless cloud updates.
• Strong “Power of One” single-data-source architecture.
• Excellent mobile capabilities for workers on the move.

B. Cons

• Premium pricing model.
• Less flexibility for highly niche, manual shop-floor work rules.
• Heavy reliance on stable internet connectivity for all functions.

C. How the vendor meets the unique requirement

• Certified for Singapore AIS (Auto-Inclusion Scheme) for employment income.
• Robust diversity and inclusion tracking relevant to Singapore’s multi-ethnic workforce.
• Visit Workday

5. Clockgogo

A. Pros

• Specialized in high-accuracy time and attendance tracking.
• Innovative “CWS” technology to prevent “buddy punching.”
• Cost-effective for companies focused primarily on attendance and payroll.

B. Cons

• Narrower focus; lacks full-suite talent management features.
• May require integration with a separate system for full ERP functionality.
• Limited advanced predictive analytics compared to larger suites.

C. How the vendor meets the unique requirement

• Clockgogo is specifically designed for mobile workforces in Singapore’s urban environment.
• Direct API links to local payroll providers for instant attendance-to-pay processing.

Also read: Why Singapore manufacturers must embrace MES for the future

Precautions for decision makers in 2026

Selecting a system today requires a forward-looking lens that accounts for the rapid shift in the technological ecosystem.

1. Avoid Windows-Only Ecosystems:

Decision makers cannot select a system which is bound to the Windows Server ecosystem. Since all popular Large Language Models (LLMs) and agentic AI tools are running natively on Linux, systems which cannot run on Linux may become obsolete in the near future. Compatibility with containerization (like Docker) and Linux-based environments is now a prerequisite for AI readiness.

2. The Rise of Asian ERP Value:

While AIs in Asia start to catch up with those in the US, Asian ERP vendors also start to provide better ROI than household ERP names from the US or EU. These regional vendors often offer deeper localization for Asian labor laws and faster response times for local regulatory changes at a more competitive price point.

3. Agentic AI Readiness:

Ensure the HRMS has an open API architecture. The next wave of productivity will come from “AI Agents” that perform tasks across systems. If your HRMS is a “closed shop,” it will be unable to participate in the automated workflows of 2027 and beyond.

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While Singapore’s corporate sector is moving faster on artificial intelligence (AI) than much of the world, a new PwC study suggests the country’s ambition to become an AI hub will depend less on enthusiasm and more on whether companies can fix the unglamorous foundations that turn AI spending into business results.

The headline numbers are strong. According to PwC’s Global AI Survey 2026, 67 per cent of Singapore businesses surveyed said they have a higher appetite for risk when investing in AI, compared with 41 per cent globally. Another 63 per cent said they allocate people and funding based on AI opportunities, above the global average of 51 per cent.

Also Read: AI hype, hard lessons: Where SEA’s startup capital is really going

That points to a market where artificial intelligence is no longer being treated as a side experiment. In many companies, it is increasingly influencing resource allocation, strategic planning, and competitive positioning.

The survey, conducted between July and September 2025, polled 1,217 senior executives worldwide. The Singapore sample was far smaller, comprising 30 respondents from publicly listed companies with annual revenue above US$100 million. Even so, the local figures reinforce the view that Singapore’s large enterprises are leaning into AI more aggressively than peers elsewhere.

Beyond pilots and buzzwords

One of the clearest signs of that shift is how Singapore companies are using AI to push beyond their traditional markets. The study found that 43 per cent of local respondents are deploying artificial intelligence to compete outside their sector, compared with 20 per cent globally.

That matters because it suggests AI is being used not just to optimise operations, but to reshape how companies think about growth. In practice, this could mean banks behaving more like software companies, manufacturers building data-driven services, or incumbents using AI to enter spaces that previously sat outside their core business.

Singapore also appears to be moving faster on the less visible infrastructure work that often determines whether AI projects scale or stall. Thirty per cent of respondents said their organisations have eliminated outdated IT infrastructure, versus 18 per cent globally.

Legacy systems rarely grab headlines, but they are one of the main reasons AI efforts remain trapped in pilot mode. Companies cannot scale advanced AI if their data is fragmented, their systems cannot integrate, or their infrastructure is too old to support modern workloads. On that front, Singapore seems ahead of the curve.

The study also suggests local businesses are pushing into more advanced AI deployments. Globally, 37 per cent of companies are still focused on relatively basic applications such as analysis, prediction and recommendation. In Singapore, that figure is just 20 per cent. Meanwhile, 17 per cent of Singapore respondents said they are using AI in autonomous or self-optimising ways, compared with 8 per cent globally.

That indicates a market that is beginning to move from AI as a support tool towards AI as an operating layer embedded in decision-making and workflows.

The gap with global AI leaders

Still, the bigger story lies in what happens when Singapore is compared not with the global average, but with the top 20 per cent of companies in PwC’s study, classified as “AI leaders”.

These firms are generating 7.2 times more AI-driven revenue and efficiency gains than peers on an industry-adjusted basis. They are also 2.5 times more likely to invest heavily, 2.4 times more likely to maintain reusable AI components across the organisation, and 1.7 times more likely to have high-quality data readily available for priority artificial intelligence applications.

In other words, the companies pulling ahead are not simply spending more. They are building stronger operating foundations, reusing what works, and aligning AI efforts with real business priorities.

Also Read: Kickstarting your AI journey: How to avoid the million-dollar mistakes most companies make

That is where Singapore’s weaknesses become clearer.

Only 53 per cent of Singapore businesses surveyed said they have robust, up-to-date security in place to protect data, AI models and infrastructure. Among AI leaders, that figure rises to 69 per cent. Just 47 per cent of Singapore respondents have a documented responsible AI framework, compared with 63 per cent of AI leaders, while only 43 per cent have a cross-functional AI governance board, versus 64 per cent among top performers.

The gaps extend to data. Just 37 per cent of Singapore firms said they maintain a single, trusted record of critical data, and 40 per cent said they use structured data effectively. Among AI leaders, those figures stand at 59 per cent and 60 per cent respectively.

Workflow redesign is another weak point. Only 37 per cent of Singapore companies said they had redesigned workflows to integrate AI rather than simply layering tools onto existing processes. Among AI leaders, 56 per cent had done so.

That shortfall is significant. AI rarely delivers meaningful gains by being bolted on top of old systems and ways of working. Its value tends to emerge when companies rethink processes from the ground up.

Policy support is growing, but companies still have to deliver

The findings arrive as Singapore steps up its national AI agenda. In 2025, IMDA and the AI Verify Foundation expanded efforts to build trust in generative AI through the Global AI Assurance Pilot. Budget 2026 added further momentum with a National AI Council, AI missions in advanced manufacturing, connectivity, finance and healthcare, as well as regulatory sandboxes and tax incentives.

That policy push strengthens Singapore’s position as a serious AI market. But it does not close the execution gap inside companies.

Anthony Dias, AI Hub Leader at PwC Singapore, said the best performers are distinguished not by the size of their spend, but by how deliberately they deploy artificial intelligence . “The companies achieving the highest AI-driven returns globally are distinguished not by how much they spend, but by how deliberately they operate: making targeted choices about where AI creates value, embedding it into core workflows, and scaling what works consistently across the enterprise.”

That is the central challenge for Singapore. The country has momentum, capital, regulatory support, and a business community that is more AI-forward than many global peers. But hub status will not be secured by ambition alone.

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

For startups, enterprise vendors and investors in Southeast Asia, the message is straightforward: the biggest opportunities may lie less in AI hype and more in solving the enterprise basics — governance, trusted data, infrastructure modernisation and workflow redesign.

Singapore has shown it is serious about AI. The next test is whether its companies can turn that seriousness into repeatable business outcomes. Right now, the country looks like a strong contender for AI hub status — but not the finished product.

The post The missing link in Singapore’s AI strategy appeared first on e27.

The shift from SEO to AEO: Why AI visibility matters in 2026

The growing importance of Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) in the Singapore marketing landscape has undergone a massive transformation. From 2024 to 2026, the industry sentiment toward AI-driven search has turned from initial doubts into absolute faith. As users increasingly rely on AI assistants to find information, brands realize that appearing in traditional search results is no longer enough; they must be visible within the generative responses that now dominate the digital discovery process.

The limitations of legacy SEO tools in the AI era

While SEO agencies often stick with legacy tools due to habitual behavior and established workflows, this loyalty is beginning to hurt both agencies and their clients. Household names like Ahrefs or SEMRUSH were built for a world of blue links, and their recent attempts to pivot often result in an incapability to track true AI visibility accurately. When combined with their increasingly insane pricing schemes, these traditional platforms are becoming a bottleneck for Singaporean brands aiming to master the new frontier of AI SEO.

Analyzing the top 3 AI visibility tools for Singapore

As the market matures, three platforms have emerged as the primary contenders for brands looking to monitor their presence in AI search. In this comprehensive comparison, we now analyse 3 popular AI visibility tool being used for AI SEO in Singapore: 

BuildSOM:

 Workduo.ai:

Semrush:

Each of these tools claims to offer insights into how AI models perceive and recommend your brand, but their execution, data depth, and regional support vary significantly for businesses operating in Southeast Asia.

Also read: Why traditional SEO is dying in Singapore — and how AISEO pioneers are winning the next Blue Ocean

Our testing methodology: A multi-region, multi-lingual brand case study

To provide a realistic comparison, we established a rigorous test case reflecting a typical Singaporean enterprise expansion. The test involves a brand holding three distinct domains. The goal is to promote the brand to English and Mandarin speakers in Singapore, as well as English, Cantonese, and Mandarin speakers in Hong Kong. We monitored four specific AI models: ChatGPT, DeepSeek, Google AIO, and Google AI Mode. The scope included 10 unique prompts per language per region, totaling 20 prompts for Singapore and 30 for Hong Kong.

Cost efficiency and monthly investment comparison

When evaluating the financial commitment required for these tools, we looked at the monthly cost based on an annual subscription. The pricing structures vary wildly, ranging from straightforward prompt-based billing to complex, multi-layered domain charges.

• BuildSOM: At USD229 per month for the Standard Plan, this covers the total 50 prompts needed for our test case. It offers a transparent, all-in-one pricing model that fits the multi-lingual requirements of the Singapore and HK markets.
• Workduo.ai: The Pro Plan is required at USD299 per month. This is necessary to accommodate the 200 daily AI responses generated by our test (4 AI models multiplied by the 50 total regional prompts).
• Semrush: This is the most expensive and complex option. Users must first pay USD139 for a Pro SEO plan. On top of that, Semrush charges per domain. For three domains in Singapore (3 Base plans) and three in Hong Kong (6 Base plans, as each covers only 25 prompts), the total balloons to USD1,030 per month.

AI response accuracy: Testing regional and linguistic nuance

The value of an AI SEO tool lies in its ability to replicate the actual user experience. If a tool cannot simulate a local user in Singapore or Hong Kong, the data it provides is functionally useless for optimization.

• BuildSOM: This tool excels by running non-English prompts on the corresponding language settings. For example, it runs Mandarin prompts on a Simplified Chinese device environment and Cantonese on Traditional Chinese settings. The results collected are identical to what a human buyer would see.
• Workduo.ai: Fails to account for local device settings. It runs non-English prompts on an English-configured device environment, leading to misleading results that do not reflect the actual local AI output.
• Semrush: Similar to Workduo, it relies on English device configurations for non-English queries. This lack of linguistic localization renders the data useless for brands targeting the Mandarin or Cantonese-speaking demographics in Singapore and HK.

AI model coverage: Which engines are being monitored?

A strong visibility tool should cover various AI models. This includes Western models and emerging models like DeepSeek.

• BuildSOM: The Standard Plan includes ChatGPT, Gemini, Google AIO, Google AI Mode, DeepSeek, and Perplexity. This exceeds the requirements of the test case.
• Workduo.ai: The Starter Plan mentions ChatGPT and Google AIO. The Pro Plan does not explicitly name the five supported models. Users may not know which models are supported until after purchase.
• Semrush: This tool supports ChatGPT, Google AI, Gemini, and Perplexity. It lacks support for Google AI Mode and DeepSeek.

Historical data and period coverage

Tracking performance trends is crucial for long-term AI SEO strategies. The period of historical data each tool provides was compared.

• BuildSOM: Users can analyze AI responses at a daily level for up to 360 days.
• Workduo.ai: Analysis is limited to the last 30 days of data, which is insufficient for long-term SEO campaigns.
• Semrush: Detailed AI responses can be tracked for up to 60 days. This is better than Workduo, but less than BuildSOM.

Final verdict: Comparing the top 3 AI visibility tools

The best tool depends on the need for accuracy and the range of AI models you want to influence. Specialized tools provide deeper insights into generative search, especially for the multi-lingual Singapore market.

Choosing the best fit for your brand

Each AI Visibility tool has its own focus. Users should evaluate these platforms based on their needs. Factors include target regions, languages, number of domains, and the specific AI models.

Why we write this article

PRbyAI aims to share updated market news using our team’s tech knowledge, helping B2B customers make informed decisions.

About PRbyAI

PRbyAI is a tech-driven Martech startup leveraging cutting-edge AISEO to help customers generate leads and tap into new markets.

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Local fractional marketing agency OtterHalf has launched Ottie’s Splash & Sell Marketing Workshop, a hands-on workshop where kids learn the basics of marketing through play. Designed for children aged 7–12, the workshop brings marketing concepts to life through play. Kids will learn how brands attract attention, design their own eye-catching posters, and perform a confident sales pitch in a fun, supportive environment.

Hands-on learning through real-world marketing and play

Held in OtterHalf’s studio at 195 Pearl’s Hill Terrace, the workshop introduces participants to basic marketing concepts using real-world examples such as KPOP Demon Hunters and Labubu. Participants also play OtterHalf’s original card game, Ottie’s Fishy Business, which subtly introduces common marketing tactics such as influencers and word of mouth.

Each workshop includes:

• Real-world discussions of trending brands, shows, and toys
• Group play time using OtterHalf’s marketing-themed card game, Ottie’s Fishy Business
• A poster competition, where participants pitch to each other and canvass for votes

Building confidence and communication skills in the next generation

OtterHalf founder Cassandra Ong believes marketing and communication are important skills for the next generation.

Parents have praised the format for encouraging communication and creativity, while educators see it as a refreshing alternative to traditional business education.

From card game success to real-world marketing lessons

Ottie’s Fishy Business achieved breakout success using only organic marketing and word-of-mouth, selling over 100 units within 3 months and reaching more than 300 households across Singapore.

The game introduces marketing concepts such as branding and storytelling, influencer marketing, competitive tactics, business ethics, and campaign planning.

Expanding the programme with future workshops and experiences

Looking ahead, OtterHalf plans to expand with additional workshops including a sales workshop and a supervised virtual mall experience.

All participants receive a certificate and earn the title “Mini Marketer”.

About OtterHalf

OtterHalf is an award-winning fractional marketing agency based in Singapore, helping businesses unlock senior-level marketing strategy and execution without the full-time cost.

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Southeast Asia’s startup ecosystem continues to mature rapidly, with founders increasingly building companies that operate across multiple markets and time zones from day one. As businesses scale across borders, access to global talent has become a key advantage, allowing companies to build distributed teams that bring together specialised skills from different parts of the world. However, hiring internationally often introduces complex challenges, from navigating local employment laws and payroll systems to managing tax requirements and benefits across multiple jurisdictions.

To successfully build a global workforce, companies need solutions that simplify global hiring while ensuring compliance with local regulations. This need is particularly relevant for startups and scaling businesses that want to access international talent without creating operational complexity. Remote addresses these challenges by providing a platform that enables companies to find, hire, manage, and pay employees anywhere in the world while remaining compliant with local employment laws.

Meeting Remote at Echelon Singapore 2026 offers founders, HR leaders, and operators an opportunity to understand how global hiring can be managed more smoothly and compliantly. Whether building distributed teams across Southeast Asia or hiring specialised talent globally, understanding how to navigate international employment regulations and workforce management across borders can help organisations scale with greater flexibility and confidence.

Enabling global teams through compliant hiring

Remote is a global HR platform designed to support international hiring and workforce management. Its platform brings together key functions such as global payroll, international hiring, benefits administration, and compliance management into a single system that helps organisations manage distributed teams more efficiently.

The company’s mission is built around a simple principle: great talent exists everywhere, and companies should be able to work with the best people regardless of location. By removing barriers associated with international employment, Remote enables businesses to expand their talent pool and hire across borders with greater confidence.

This approach is particularly relevant for startups and scaling companies in Southeast Asia and the wider Asia Pacific region. As businesses in markets such as Singapore, Australia, Indonesia, Malaysia, and the Philippines continue to expand internationally, the ability to build distributed teams can play a key role in expanding into new markets, accessing specialised skills and supporting rapid growth.

Remote’s platform provides organisations with tools to manage employment contracts, payroll processing, tax requirements, and employee benefits in different jurisdictions. This integrated approach helps companies maintain compliance with local regulations while reducing the operational complexity associated with managing global teams.

Meet Remote at Echelon Singapore 2026

Remote joins Echelon Singapore 2026 as the Preferred Remote Hiring Partner, alongside founders, investors, corporates, and ecosystem leaders gathering at Suntec Singapore CEC on 3–4 June 2026. The event brings together Southeast Asia’s startup and technology community through content stages, exhibitions, networking opportunities, and knowledge sharing sessions designed to support regional innovation and growth.

Attendees can connect with the Remote team during the exhibition to learn more about building distributed teams and managing international hiring across the Asia Pacific region. For startups and SMEs navigating talent shortages or looking to expand internationally, conversations around compliant global hiring and workforce management are becoming increasingly important. Platforms like Remote play a role in enabling organisations to access talent beyond geographic boundaries while maintaining operational clarity and regulatory compliance.

As Southeast Asia’s technology ecosystem continues to evolve, access to global talent will likely remain a key factor in how companies scale and compete internationally. Events such as Echelon Singapore provide a space for founders, operators, and technology partners to exchange ideas and explore tools that can support the next phase of regional growth.

The region is evolving quickly, and Echelon 2026 offers the right place at the right moment to be part of what comes next. Register here to join the conversation.

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In the rapidly evolving digital landscape, the traditional boundaries of search engine optimization are expanding. For years, businesses focused almost exclusively on keyword rankings and backlink profiles. However, as generative AI becomes a primary interface for information gathering, a new discipline has emerged: Brand Mention Tracking within the context of artificial intelligence. This practice involves monitoring how often and in what context a brand is cited by Large Language Models (LLMs) and AI-driven search engines.

What is brand mention tracking?

Brand mention tracking is the process of monitoring online conversations, citations, and references to a specific company or product across the digital ecosystem. Traditionally, this meant tracking social media, news outlets, and blogs. Today, the scope has broadened to include “AI Visibility.” This refers to how prominently a brand appears when users query AI tools like ChatGPT, Google Gemini, or DeepSeek.

For modern enterprises, tracking these mentions is no longer just about reputation management; it is about data-driven SEO. It requires understanding the sentiment and frequency of brand citations within the datasets that train and inform AI. By analyzing these patterns, businesses can identify whether they are perceived as industry leaders or if they are being overlooked by the algorithms that currently guide consumer decisions.

Why brand mention tracking is essential for SEO and GEO in Singapore

For startups and established firms in Singapore, the shift toward Generative Engine Optimization (GEO) is particularly significant. Singapore serves as a hyper-competitive global hub where consumers are highly tech-savvy and quick to adopt AI assistants for local service recommendations, financial advice, and product research.

In this environment, Brand Mention Tracking serves as the backbone of a successful SEO and GEO strategy. If an AI tool does not “mention” your brand when a user asks for the “best fintech solution in Singapore,” you effectively do not exist in that user’s journey. High visibility in AI responses establishes authority and trust—two pillars of the EEAT (Experience, Expertise, Authoritativeness, and Trustworthiness) framework that search engines now prioritize. Furthermore, because Singapore is a multicultural gateway, tracking mentions across different linguistic and cultural contexts is vital for capturing the full breadth of the market.

Also read: What the top 10 time attendance systems in Singapore tell us about workforce management in 2025

Why BuildSOM is the leading SEO tool for GEO targeting Singapore

When evaluating the best SEO tool for GEO targeting Singapore, BuildSOM has gained significant traction among marketing experts for its specialized approach to AI visibility. Unlike legacy tools that often treat AI as a secondary feature, BuildSOM is built specifically to bridge the gap between traditional search and the new era of LLMs.

Key advantages of utilizing BuildSOM for your visibility strategy include:

• Native Non-English Monitoring: It is the only global tool offering native non-English AI visibility monitoring. This is crucial for Singapore’s diverse market, as it monitors responses in Malay, Chinese, and other languages through native environments rather than simple translation.
• Realistic Consumer Simulation: Instead of relying solely on LLM APIs, BuildSOM captures results through the actual browser UI, simulating a true consumer journey for more reliable data.
• Broadest LLM Coverage: It provides extensive coverage across various platforms, including DeepSeek and Doubao, which are essential for reaching the non-English speaking and regional communities.
• Dual-Market Insights: It is uniquely positioned to provide AI visibility data both inside and outside of China, a critical feature for Singaporean exporters and retailers.
• Cost-Efficiency: The brand-based fee scheme makes it significantly more affordable than legacy SEO tools, often costing only 10-30% of traditional alternatives.
• Practical Dashboard: The interface is designed for “prompt gap analysis,” allowing marketers to spot “missed AI opportunities” through a comprehensive birdview matrix.
• Accessibility: Startups can begin with a free account without any credit card commitment, allowing for immediate exploration of AI response monitoring.

Explore more useful tools for GEO:

1. BuzzSumo: Excellent for identifying trending topics and influential mentions across social platforms to inform content strategy.
2. Semrush: A comprehensive suite for traditional keyword research and competitive analysis that complements AI visibility data.
3. AnswerThePublic: A tool that visualizes search questions and suggested images, helping creators understand the specific queries fueling AI responses.

Also read: Costing comparison of top 7 popular ERP software for food manufacturing in Singapore

How to utilize an AI visibility report (AVR) to improve brand mention tracking

An AI Visibility Report (AVR) is a diagnostic tool for any SEO professional. To improve brand mention tracking, you must first analyze the “Prompt Gap.” This involves identifying the specific queries where competitors are mentioned by AI, but your brand is not.

By reviewing an AVR, you can determine the specific sentiment associated with your brand. If the AI recognizes your brand but provides outdated information, you can adjust your PR and content distribution to ensure fresher data is available for AI crawlers. Additionally, the AVR helps in “Cross-Cultural Surveillance,” ensuring that your brand’s authority remains consistent across different languages and regional LLMs. Using these reports allows you to move from reactive monitoring to proactive optimization, ensuring your brand is the “top of mind” choice for AI engines.

The competitive risk for Singapore startups

For Singaporean startup founders, the window to claim “AI Real Estate” is closing. As LLMs become more entrenched in the daily lives of consumers, those who fail to establish their AI visibility now risk being permanently excluded from the digital conversation. Waiting to build an AI Visibility Report is no longer an option; it is a delay that allows competitors to define the narrative of your industry.

The cost of inaction is high, yet the barrier to entry is low. You can set up a free account today with no credit card commitment. Exploring how an AI Visibility Report works will provide you with the raw data needed to protect your brand and dominate the future of search. Don’t let your brand become a ghost in the machine—start tracking your AI presence today.

Why we write this article

PRbyAI aims to share updated market news using our team’s tech knowledge, helping B2B customers make informed decisions.

About PRbyAI

PRbyAI is a tech-driven Martech startup leveraging cutting-edge AISEO to help customers generate leads and tap into new markets.

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