The semiconductor boom is no longer just about building more fabs; it’s about keeping the tools inside them running with near-perfect reliability. As global chipmakers pour billions into new plants and equipment, the spotlight often stays on the giants: the OEMs, the mega fabs, and the trillion-dollar supply chains.

But behind every high-performing production line is a quieter support layer of specialist SMEs. These firms refurbish critical tools, reduce downtime, and solve the operational problems that can make or break shipment schedules.

Also Read: Semiconductors at risk: The invisible threats that could break global supply chains

Singapore-based Global TechSolutions (GTS) is one such company. In this Q&A, its CEO Kenneth Lee Wee Ching, shares how the company has built a regional footprint across Singapore, Malaysia, Taiwan and the US — and why reliability, auditability, and near-site agility are becoming just as strategic as the next breakthrough node.

Edited excerpts:

Semiconductors are heading toward a trillion-dollar market, and fabs are pouring money into tools. Yet the “support layer” gets little attention. In one sentence, what does GTS do that directly protects fab revenue, and why can’t big OEMs do it as effectively?

At its core, GTS restores and upgrades front-end semiconductor tools to OEM-equivalent—or better—hardware performance, helping fabs reduce downtime, bypass long new-tool lead times, and protect shipment schedules. Large OEMs are not structured for the same near-site agility, deep customisation, or selective execution model where we only take on work we can certify to OEM-level outcomes with warranty. That focus is why our work sits so close to protected fab revenue.

For founders and VCs, defensibility matters. In semiconductor equipment services, what is your moat?

Our defensibility stems from a combination of breadth of capability, execution discipline, and customer proximity. GTS is among the few regional players offering a full suite of new equipment, refurbishment, upgrades, and field engineering—supported by cleanroom-certified facilities and test platforms that simulate real fab environments.

Our footprint across Singapore, Malaysia, Taiwan, and the US enables near-site response and supports a “close-to-customer” strategy that holds up even when supply chains tighten. We pre-stage critical spares, run parallel testing, and compress time-to-qualification without compromising performance.

Equally important is know-how. We maintain proprietary jigs, fixtures, firmware, and automated test routines developed in-house. We also deliberately decline work if we cannot meet OEM-equivalent standards. That discipline preserves trust and yields for customers.

Together, these capabilities address cost, lead time, customisation, and sustainability — while reinforcing defensibility in an industry where reliability, repeatability, and auditability are non-negotiable.

What does “reliability” mean in numbers? Which metrics matter most to customers, and what improvement ranges are realistic?

We define reliability using metrics that production and finance teams already care about. These include Mean Time Between Failures (MTBF), time-to-qualification (the speed at which a tool is released back to production) and chamber-level performance indicators such as thermal uniformity, vacuum stability, and gas-flow calibration, all of which underpin line yield.

Also Read: Indonesia courts Nvidia and AWS as it eyes a bigger role in global chip supply chains

In advanced-packaging-relevant lines, customers have seen roughly a 15 per cent reduction in downtime and around a 7 per cent improvement in line-yield stability following refurbishment and targeted upgrades. We also treat documentation completeness as a KPI. Clean, ISO- and SEMI-aligned documentation with traceable test logs shortens audits and keeps production lines compliant.

Trust is the real currency in semiconductors. How did GTS win its first serious customers?

Trust in this industry is built incrementally. Qualification standards are stringent, and introducing a new partner involves lengthy approval cycles. We started with smaller scopes and incremental improvements, then expanded into more complex parts and equipment only after demonstrating consistent results.

That caution is necessary: with thousands of steps in chipmaking, minor errors can cascade into significant losses. We also operate under strict SOPs and controlled environments, including Class 100 and Class 1,000 cleanrooms that mirror fab conditions. This reassures customers that our processes behave predictably when deployed on production lines.

Over time, that translated into a track record of high performance at optimised cost—combined with faster turnaround and greater customisation than traditional OEM approaches. Customer retention and referrals followed naturally.

How do you operate through uncertainty, like export controls, shifting trade rules, audits, and supply disruptions, without breaking delivery promises?

Semiconductors are deeply intertwined with geopolitics. Export controls, tariffs, and regulatory shifts often translate directly into supply-chain disruptions. To manage this, GTS built a “global supply chain mirroring” approach years ago.

We maintain engineering presence, parts strategy, and execution capability close to where customers operate, while aligning closely with them on technology roadmaps and requirements. Where appropriate, this allows us to localise execution and rely on locally available parts rather than a single cross-border supply route.

When sudden policy changes occur, this resilience prevents disruption from becoming downtime. Even when the “cleanest path” is no longer available, our proximity and documentation discipline allow us to align on acceptable alternatives with customers and keep execution controlled, predictable, and auditable.

Are fabs shifting from break-fix to predictive reliability engineering? What must SMEs build to stay relevant?

Yes, the shift is underway, especially in high-mix lines where advanced packaging intersects with front-end steps. Maintenance is moving from reactive break-fix toward predictive diagnostics and reliability engineering.

For SMEs, relevance requires a capability stack that includes high-quality data capture, component-level cleanroom testing, predictive diagnostics tied to known failure modes, and disciplined teardown-to-QA loops. Just as necessary is documentation that integrates cleanly into fab workflows so insights translate into approvals and action.

Also Read: Thailand enters the chip race, without challenging Singapore head-on

At GTS, we’re investing in software-driven diagnostics and fault prediction. We are developing systems that learn from troubleshooting patterns and equipment signals (such as vibration, motion anomalies, and acoustic changes) to detect early warning signs. In parallel, we’re standardising knowledge-sharing across regions so improvements in one site can be replicated quickly elsewhere without compromising quality controls.

How are AI and advanced packaging changing customer demands, and what must SMEs build by 2026?

AI workloads and advanced packaging are raising expectations across the board. We’re seeing demand for tighter thermal control and film uniformity, more line-specific modifications instead of generic upgrades, faster ramp-to-production timelines, and deeper metrology and certification discipline.

To stay relevant through 2026, SMEs need modular upgrade paths, cleanroom testing capacity, predictive diagnostics, and documentation that is export-control-ready and audit-friendly. Our focus remains on improving chamber-level reliability with auditable performance, so innovation reaches production with stability—not just speed.

At the same time, software-driven capabilities will become increasingly important, enabling customers to shift from reactive fixes to earlier, proactive interventions as tolerances tighten.

From a scaling perspective, what breaks first when an SME expands across countries? What do you standardise, and what stays local?

The first thing that breaks is consistency of execution—not technical skill, but how reliably teams diagnose issues, control variation, and sign off outcomes under pressure. Small differences in training or test interpretation can create big swings in customer confidence.

To prevent this, we standardise the “spine” of delivery across all regions: ISO- and SEMI-aligned quality systems, refurbishment and test protocols, structured documentation and sign-off gates, training pathways, and parts qualification strategies. This ensures predictable quality without reinventing processes at each site.

What remains local is how we integrate into each customer’s operating reality—site-specific compliance requirements, fab conventions, and coordination with local stakeholders. The goal is a common engineering playbook with local fluency.

Looking to 2026, what’s your base case and contrarian view for the semiconductor services ecosystem? Who wins?

Our base case is that advanced packaging continues to scale and fabs increasingly prioritise predictability—uptime, faster qualification, and auditability—over pure capex expansion. Service partners that win more scope will be those that can consistently return tools to certified performance and prove it with clean documentation.

Also Read: ‘The future of semiconductor manufacturing is regional’: Global TechSolutions CEO

The contrarian view is that even when equipment is available, execution friction becomes the real bottleneck. Compliance overhead, export controls, and specialist talent shortages may matter more than hardware availability. In that environment, speed alone doesn’t win; controlled agility does—the ability to move fast while remaining auditable and safe.

Across both scenarios, the biggest pinch points will be human capital, compliance, and critical spares. Talent is particularly challenging given the industry’s complexity, which makes ecosystem-building and collaboration essential.

On winners, it’s not simply niche specialists versus scaled platforms. It’s whichever model can repeatedly prove outcomes—performance, auditability, and production stability—again and again.

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AI agents are increasingly presented as the next leap in industrial transformation — systems that can move beyond analysing data to making decisions and taking action autonomously.

In theory, they promise a future where manufacturing becomes faster, smarter and more adaptive.

But in food manufacturing, there is a harder truth we need to confront first: AI cannot fix processes we do not yet fully understand.

Manufacturing is not a stable system

This is especially evident in food systems, where manufacturing is far from stable.

Unlike highly controlled digital environments, food production deals with biological raw materials that are inherently variable. Moisture content shifts with storage conditions. Protein functionality changes depending on source and prior processing history.

Small differences in formulation or temperature can lead to significant changes in final product quality.

Take extrusion, for example — a process commonly used to produce puffed snacks and plant-based protein products.

A successful outcome depends on balancing moisture, temperature profile, screw configuration and ingredient behaviour with precision. When conditions align, the product expands and forms as intended. When they do not, the result may collapse, become dense, or fail to form the desired structure.

These are not rare anomalies.

They are part of the everyday reality of manufacturing.

The promise of AI — and what it assumes

In my own work at SIT, including pilot-scale trials at FoodPlant, I am often asked whether AI can be used to predict outcomes or recommend processing conditions in extrusion.

It is an understandable question. If enough production data is collected, it seems reasonable to expect that AI should be able to identify patterns and optimise performance.

Also Read: Beyond the buzz: How AI and sustainability are reshaping design, manufacturing, and construction in APAC

In principle, this promise is compelling.

It suggests a shift from trial-and-error towards more predictive, data-driven manufacturing.

But this vision rests on a critical assumption: that the data available fully captures how the system behaves.

In food manufacturing, that assumption rarely holds.

Where the gap lies

AI systems can only learn from what is measured.

Yet some of the most influential variables in food processing — such as how materials behave under heat, pressure or shear — are not always directly observed or consistently recorded. Many process interactions remain tacit, built through experience rather than explicit data capture.

Even when data exists, relationships between variables are often non-linear and context-dependent.

The same processing condition can produce different outcomes depending on formulation, material history, or environmental conditions.

What AI receives, therefore, is often only a partial and unstable representation of reality.

When AI performs poorly in such settings, the conclusion is often that the technology is not mature.

In many cases, the issue lies elsewhere.

We are asking algorithms to optimise systems that remain insufficiently characterised.

More data is not the same as a better understanding

There is growing emphasis on shared datasets, digital toolboxes and industrial AI platforms.

These are important developments — but more data alone does not resolve the underlying challenge.

If variables are defined differently, measured inconsistently across facilities, or recorded without a common structure, combining datasets does not improve understanding.

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

It amplifies inconsistency.

A meaningful dataset — much like a well-designed dashboard — reflects a clear understanding of what variables matter and how they relate to outcomes.

Without that structure, aggregating more data does not lead to better insight.

It simply scales the same limitations.

Why this matters now

These questions extend beyond manufacturing efficiency.

For Singapore, they are becoming increasingly relevant as food resilience rises on the national agenda. Recent geopolitical tensions and disruptions to global supply chains have once again highlighted how vulnerable food systems can be under external shocks.

Singapore imports more than 90 per cent of its food.

In such a context, resilience cannot be defined only by where supply comes from.

It must also include our ability to convert available inputs into stable, nutritious and scalable food products locally.

That capability is a resilience multiplier.

What needs to be built

AI can play an important role in this future.

It can accelerate learning, improve consistency and help detect patterns that are not immediately obvious.

But AI is not the foundation.

Before autonomous systems can make reliable decisions, manufacturing systems must first become more observable, more structured and better understood.

This means:

• Better characterisation of material behaviour
• Clearer definition of operating windows
• More consistent ways of capturing process–material interactions

Also Read: Anomaly Bio powers the future of ingredient manufacturing with US$2.6M in pre-seed funding

Only then can AI move beyond pattern recognition into dependable decision support.

Beyond automation: Building real capability

The future of intelligent manufacturing will not be built by algorithms alone.

It will be built on a deeper mastery of process.

Until we close that gap, AI will not transform manufacturing. It will simply make visible how much of it we still do not fully understand.

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Singapore-based quantum software startup Horizon Quantum Computing has completed its merger with dMY Squared, securing a Nasdaq listing and roughly US$120 million in gross proceeds.

The deal gives the company a far bigger war chest than it had as a private startup and offers an early test of whether investors are ready to back deep-tech SPAC stories again.

Also Read: Can SPACs avoid another reverse merger crisis?

Horizon Quantum Computing began trading on Nasdaq on March 20 under the ticker HQ after closing its business combination with dMY Squared Technology Group, a special purpose acquisition company, or SPAC.

For Horizon, the attraction of a SPAC was fairly straightforward: speed, price certainty and access to US public-market investors without the longer, more fragile runway of a traditional IPO. That matters even more for a company like Horizon, which is building software infrastructure for quantum computing — a field long on promise, short on near-term revenues, and still difficult for mainstream investors to value using conventional yardsticks.

The Singapore-founded startup said the transaction delivered about US$120 million in gross proceeds before expenses. That is a meaningful jump from the roughly US$21 million in publicly disclosed private funding Horizon had raised since its founding in 2018, making the de-SPAC by far its biggest capital event to date.

The company plans to use the cash to expand research and development, build out its hardware testbed, and advance its quantum programming platform, Triple Alpha.

Chief executive and founder Joe Fitzsimons framed the listing as a bet that quantum computing would reach a turning point. “The field is reaching an inflection point,” he said, pointing to recent progress in quantum hardware and error correction.

That is the optimistic view. The harder question is whether public investors will buy into it.

Quantum computing has attracted increasing attention as companies race to turn laboratory advances into usable systems, but commercial timelines remain hazy. Horizon’s pitch is that it does not need to bet on one winning hardware architecture. Instead, it is building hardware-agnostic software tools that could sit above whichever quantum machines eventually scale.

That positioning also helps explain why a SPAC made sense. Unlike a conventional IPO, the SPAC route has historically given emerging technology companies more room to tell a forward-looking story, particularly when current revenues do not yet capture the scale of the opportunity they are chasing.

Still, Horizon Quantum’s deal should not be read as proof that the SPAC market is suddenly back in full force after its long slump. The frenzy of 2020 and 2021 ended badly for many companies, with poor post-listing performance, tighter regulation and rising interest rates draining enthusiasm from the structure. What has emerged since is not a broad revival, but a more selective market in which investors are willing to revisit deals with clearer strategic logic.

In that sense, Horizon Quantum looks less like a sign of another SPAC gold rush and more like a targeted exception: a deep-tech company with a specialised narrative, a US listing ambition and a need for substantial capital.

The transaction also adds Horizon to a still short list of Singapore-headquartered companies that have reached public markets through SPAC mergers. Publicly known examples include Grab, PropertyGuru, Bitdeer, and ESGL; with Horizon, the number is at least five.

That count is notable because Singapore has produced relatively few de-SPAC listings compared with the wave seen in the US, even as the city-state has become an increasingly important base for regional technology and deep-tech startups. Grab and PropertyGuru were consumer internet names. Bitdeer was tied to digital assets. Horizon now brings quantum software into that small club — a very different bet, and arguably a riskier one.

Also Read: The hidden danger in SPACs. Is the hype worth the risk?

The broader implication is that Southeast Asian startups are still willing to use alternative paths to the public market when a standard IPO does not quite fit. Whether that becomes more common again will depend less on nostalgia for the SPAC boom than on whether newly listed companies can show discipline after the bell rings.

For Horizon, the immediate milestone is clear enough: it now has Nasdaq access, fresh capital, and public-market scrutiny. The harder part begins now — proving that quantum software infrastructure can become a serious business before investor patience collapses into the nearest probability wave.

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As Southeast Asia races toward a knowledge economy, the Philippines’s situation — highlighted in the Philippine Private Capital Report 2026 by Foxmont Capital Partners — offers a focused view of enduring challenges in education and workforce skills development that resonate across the region.

The recently formed Second Congressional Commission on Education (EDCOM II) points to execution gaps in the education system as major bottlenecks that limit the country’s productivity prospects. Addressing these gaps is essential not only for national growth but for the Philippines‘ role in an increasingly interconnected ASEAN market.

Education performance and economic growth

The Philippines continues to struggle with foundational literacy and numeracy relative to regional peers. Public schools consistently underperform compared with private institutions and neighbouring systems, and international assessments and regional comparisons repeatedly underscore that many Filipino students leave basic schooling without secure reading and arithmetic skills.

Also Read: The hidden tax on Philippine SMEs: Unreliable infrastructure

Because foundational learning is strongly correlated with long-term gains in GDP per capita, these shortfalls are not just educational but are economic. A large cohort lacking basic skills constrains the workforce’s ability to progress into higher-productivity roles, slows technology adoption, and weakens the country’s ability to attract and retain higher-value industries.

Beyond outcomes: system execution matters

EDCOM II’s findings are instructive because they shift attention from outcomes alone to the mechanisms that produce them. Persistent weaknesses in curriculum delivery, uneven teacher preparation and professional development, fragmented resource allocation, and gaps in local-level implementation combine to erode the system’s effectiveness.

In many classrooms, the curriculum is ambitious on paper but poorly supported in practice: teachers lack time, materials, or targeted training to teach for mastery; assessment systems focus on rote recall rather than competency; and administrative capacity at school and municipal levels is insufficient to monitor and support improvements.

These execution problems prevent the education system from reliably converting investment into learning. In turn, that reduces the supply of mid-level skilled workers—the technicians, supervisors, and specialist operators who typically drive productivity gains in manufacturing, services, and digital sectors.

The skills trifecta for productivity

The Foxmont report sensibly frames the transformation challenge as a three-part “skills trifecta”:

1. Strong foundational learning
2. Expansion of the mid-level skilled workforce
3. Accelerated reskilling to keep pace with rapidly evolving job requirements

All three elements are mutually reinforcing. Strong foundational skills (literacy, numeracy, digital basics) enable learners to acquire more advanced technical skills faster. A larger mid-level workforce creates career pathways that make reskilling attractive and viable. And rapid reskilling systems ensure that workers can transition across firms and sectors as automation and digitalisation change demand.

For the Philippines, which has a young population but faces rapid technological disruption and stiff regional competition, failing on any one leg of the trifecta risks turning demographic advantage into a liability.

Policy levers and institutional actors in the Philippines

Several existing institutions and reforms are relevant:

• K–12 and basic education reforms were intended to improve learning outcomes by extending years of schooling and revising curricula. However, extending the school year without parallel improvements in teaching quality, assessment, and resources has a limited impact.
• Technical and vocational education and training (TVET) institutions, including government training programmes, offer a natural platform to scale mid-level skills. Strengthening linkages between TVET providers and employers — and raising quality assurance standards — can make these programs more effective.

Also Read: Philippines’s quiet AI revolution is about work, not tech

• Lifelong learning infrastructure (including online platforms, modular credentials, and recognition of prior learning) remains embryonic. Expanding flexible upskilling pathways is critical.
• Local government units play a major role in implementation. Enhancing their capacity to manage education financing, data, and partnerships is a high-leverage intervention.

Industry-education alignment: emerging examples and opportunities

Partnerships between industry and education are multiplying in the Philippines. Sectoral initiatives—particularly in electronics, semiconductors, business process services, and logistics—are collaborating with technical colleges and training centers to co-design curricula, provide equipment and internships, and certify competencies that match employer needs. These school-to-industry pipelines create clearer routes from education into employment and help ensure training content is current with workplace technology.

Scaling such models requires policy support: incentives for firms to invest in workforce development; streamlined processes for private training providers to be accredited; and mechanisms to share costs and risk between government, firms, and learners. A national skills mapping and competency framework tied to industry clusters would help scale successful pilots into systemic solutions.

The digital divide and equitable access

Any strategy to build a knowledge economy must confront inequities. Urban and wealthier areas tend to have better schools, more teachers with advanced training, and faster internet access; rural and island communities often lag.

The pandemic highlighted this digital divide and the limits of one-size-fits-all remote learning. Investments in connectivity, appropriate devices, and teacher ICT training must be matched by investments in pedagogies that work in low-bandwidth and multi-grade settings.

Inclusive policies are also required for marginalised groups: out-of-school youth, learners with disabilities, and adults who missed earlier opportunities. Strengthening the Alternative Learning System (ALS) and creating modular, stackable credentials can help these populations re-enter pathways to mid-level employment.

Financing and incentives

Sustainable reform needs predictable financing and performance-based incentives. Shifting funding toward evidence-based interventions (teacher mentoring, remedial literacy programmes, assessment systems, and employer-linked training) will yield a higher return than blanket increases in inputs. Public-private financing mechanisms, such as matching funds for employer training or sectoral skills funds, can mobilise additional resources while aligning incentives toward job-relevant outcomes.

Also Read: “Skills intelligence” is the future of hiring, says LinkedIn’s Elsie Ng

Regional implications and ASEAN coordination

The Philippines’ education and skills bottlenecks are instructive for the broader Southeast Asian region. ASEAN economies share similar pressures: rapid technology adoption, aging in some countries, youthful demographics in others, and competition for investment in higher value-added industries.

Regional coordination can accelerate solutions:

• Shared competency frameworks and skills passports to facilitate labour mobility
• Cross-border training partnerships and recognition of certifications
  Joint investments in edutech and open learning resources adapted for Southeast Asian languages and contexts
• Prioritising foundational learning across the region and creating harmonised mid-level skill standards would raise the floor and expand the pool of workers ready for technology-intensive sectors.

Measuring success: data and accountability

Improved measurement is essential. Standardised assessments of foundational learning, timely labour market data, and tracer studies of graduates help policymakers identify what works and where to target resources. Transparent dashboards that track learning outcomes, teacher deployment, and industry training metrics would strengthen accountability and enable course corrections.

Education as economic strategy

The Philippines’s education challenge is not an isolated social issue; it is central to economic resilience and inclusive growth. Fixing execution gaps in basic education, expanding mid-level skill pipelines, and building rapid reskilling systems will determine whether the country (and the wider region) can capitalise on technological opportunities or fall into a growth trap of low-value activity.

Also Read: Why are skills the currency of the future business world?

Policymakers, educators, employers, and civil society must act in concert: invest where evidence shows biggest returns, align curricula with real-world demand, and build flexible, inclusive lifelong learning pathways. If done well, the Philippines can turn its demographic potential into sustained productivity gains and play a stronger role in Southeast Asia’s knowledge economy—creating more good jobs and broader shared prosperity along the way.

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Retail tech has spent years trying to make shops behave like websites. Online, every click, hover, and abandoned basket is measured and optimised until intent becomes revenue. In a supermarket or mall, that precision often vanishes: managers rely on instinct, a few CCTV feeds and yesterday’s numbers to guess what is happening now.

The offline blind spot in a data-driven retail world

Ulisse Ltd, led by CEO Luca Nestola, argues that blindness is expensive in Southeast Asia, where competition is fierce, and service expectations are rising. Its platform, Hyperspace, is built around what he calls “Physical AI”: software that analyses how crowds move and prompts staff to act before friction turns into lost sales at the shelf and checkout.

Also Read: Why retailers must think like tech companies to thrive in a data-driven economy

Nestola’s starting point is a mismatch between the digital and physical worlds. E-commerce tracks the customer journey end to end and adjusts constantly. Physical retail, despite accounting for about 85 per cent of sales, still runs with limited visibility.

Traditional analytics, often based on cameras or manual counts, tends to answer only what happened: how many people entered, where they went, and when footfall spiked. By the time reports land, the moment has passed.

Hyperspace is meant to behave more like a live control system, matching demand with capacity as conditions shift. In-store terms, capacity is open tills, staffed counters and space devoted to promotions so managers can intervene immediately.

Privacy-first analytics: why LiDAR beats cameras

That push for real-time action is paired with an equally firm stance on privacy. Hyperspace uses LiDAR (Light Detection and Ranging) rather than cameras, and Nestola claims it enables 100 per cent customer privacy by design.

LiDAR emits laser pulses to build a 3D map; it captures shapes and motion, not personal features. In Ulisse’s model, a shopper is a moving cluster of points, a trajectory with no personally identifiable information.

Nestola argues this matters in Southeast Asia as regulators tighten rules on data use, with Singapore’s PDPA a clear signpost. It lets retailers extract sophisticated analytics without compromising anonymity, a requirement for shoppers and for legal teams approving deployments.

With cameras, even if the video is later blurred, the raw footage still exists, creating compliance burdens. With LiDAR, Ulisse says, there is no face, no skin colour, no attribute to record. “Cameras identify individuals,” he says. “LiDAR understands movement.”

Designing for chaos: Southeast Asia’s diverse retail formats

Ulisse also had to design for the region’s diverse range of store formats. Hyperspace is layout-agnostic, built around a 3D Venue Builder and a LiDAR Coverage Planner. Retailers can upload a 2D architectural drawing, typically a DWG file, and the system parses it to generate an accurate 3D digital twin.

If plans do not exist, Ulisse can quickly create the layout using manual tools. Coverage is provided by scalable sensor fusion: multiple low-cost LiDAR units are installed, and their streams are stitched into a seamless view of the venue. The approach is meant to work across extremes, from a 100-square-metre convenience shop to a 10,000-square-metre hypermarket, and from tight city aisles to open-plan big-box floors, without sacrificing tracking quality or operational usefulness.

Operational gains at scale: where small improvements compound

Nestola sees Southeast Asia’s big opportunity as operational efficiency at scale. Retail is intensely competitive, and margins are often thin, so small gains in throughput and revenue per square metre compound quickly.

Hyperspace focuses on two daily drains on profit.

Queue management is first: the platform predicts queue formation and alerts staff to open additional checkouts before lines become long enough to trigger abandonment.

Second is staff and space allocation. By showing where customers are, and where they are not, managers can move staff to the right zones and rework promotions or layouts to monetise underused space. The pitch is practical: improve performance without a major refit so the same store can serve more shoppers each hour.

Also Read: AI shopping adoption surges 39 per cent in APAC, fueling retail tech investments

Ulisse will not name Southeast Asian clients, citing confidentiality agreements, but Nestola says pilots in the region echo work with European retailers such as Italy’s Esselunga. In one deployment with a major grocery chain in Singapore, Hyperspace was trained on checkout operations and the fresh produce section.

Ulisse says predictive alerts helped managers redeploy staff and cut average checkout wait times by 45 per cent during peak hours. The same retailer used foot-traffic and dwell-time analysis to reposition a key fresh fruit promotional display, and Ulisse reports a 22 per cent increase in category sales within the first month.

For him, the takeaway is simple: spatial analytics matters only when it becomes a decision quickly enough to change the customer experience on the floor, not paper.

Turning movement into money: decoding shopper intent

Hyperspace’s intent engine is built on what Nestola calls the “collective physics of shopping”: movement patterns, analysed at scale, become proxies for commercial intent. The system does not try to read individual psychology. Instead, it searches for repeatable signatures across thousands of anonymous trajectories.

A direct, accelerating path to the checkout signals purchase intent. Deceleration and repeated micro-stops in front of a shelf indicate consideration. A rise in dwell time and approach frequency around an endcap display versus baseline suggests promotional pull. When flow speed drops suddenly, or clusters form in odd places, Hyperspace flags friction—congestion, obstructions, confusion—so staff can intervene.

Ulisse says its core LiDAR tracking reaches over 99 per cent accuracy in detecting and continuously tracking shoppers anonymously. At the same time, queue prediction models have shown over 95 per cent accuracy in forecasting wait times—enough, Nestola argues, to act before queues become visible problems.

Measuring what matters: from footfall to causal impact

For in-store media, Ulisse offers PEBLE (Post-Exposure Behavioural Lift Engine), which aims to measure the causal impact of advertising. It compares the post-exposure behaviour of shoppers detected in front of a digital screen with that of a matched control group who were not exposed, an approach Ulisse says has been validated by Deloitte.

Hyperspace is designed to plug into existing retail systems, acting as a central nervous system. DOOH integrations can link an entrance ad for a new drink to later visits to the beverage aisle. POS links correlate traffic and dwell time with sales. Staff-management integrations route alerts to handheld devices, telling teams to open checkouts or assist in specific aisles in real time, store-wide.

Scaling without capex: a service-led business model

Ulisse’s go-to-market is shaped by cash flow realities, particularly for SMEs. Hyperspace is sold as LiDAR-as-a-Service, bundling hardware, software, installation and support into a monthly subscription with no upfront hardware bill. The model preserves capital—zero CAPEX—while delivering a typical payback period of under three months, and it lets retailers scale from one site to many without repeated big purchases.

Also Read: Chaos is a ladder: How instant retail is turning stores into fulfilment powerhouses

Even so, he expects two barriers: perceived complexity and resistance to change. Ulisse’s answer is a “30-minute deployment” playbook, automated floor-plan import and sensor placement, plus plug-and-play Ulisse Box edge servers. The system is framed as augmented intelligence: simple alerts that help managers act faster, not replacements for judgment. It targets post-pandemic shoppers who demand speed and less waiting.

Beyond retail: building a universal operating system for physical spaces

Hyperspace can monitor occupancy and flow to reduce overcrowding and help test new formats, such as dark stores. The system keeps learning as behaviour shifts. On the roadmap is AI Narrator, turning analytics into prompts—flagging that sales are down 15 per cent because an obstruction near the entrance is slowing traffic. Ulisse is focusing on grocery in Singapore, Malaysia and Thailand, then expanding into airports, malls and smart buildings with Kone as a partner. The end goal is a universal operating system for privacy-safe, high-performance spaces.

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Singapore’s digital asset market is moving into a more demanding phase, where trust, regulation and staying power matter more than momentum alone. For firms operating across multiple jurisdictions, including Singapore, this shift is changing what it takes to build in this market, and why firms shaped by stronger systems and discipline may be better placed for what comes next.

A tougher market is taking shape

Over the past year, Singapore’s digital asset market has started to feel meaningfully different. The conversation is no longer about whether the sector has a future here, but about who gets to shape it and to what standard.

That shift became especially clear in June 2025, when the Monetary Authority of Singapore stated that firms based in Singapore and providing digital token services only to customers overseas would need to be licensed from 30 June 2025, while also indicating in public communications that licenses would not be generally issued.

At the same time, MAS has continued to back work on tokenised finance through Project Guardian, which signals that Singapore still sees promise in this space, but wants it to develop on firmer and more credible ground.

I do not see that as a contradiction, but a sign of a market becoming more mature about what it wants to encourage and what it wants to avoid. For a long time, digital assets were judged by their loudest personalities and their boldest claims, which made it easy for attention to run ahead of trust.

A more regulated market changes that balance, because it puts far more weight on whether a business can explain how it works, manage risk responsibly and build something with lasting value. As someone building in this space through Caladan, I have seen how much stronger markets become when trust and discipline matter more than momentum.

How this gap became impossible to ignore

That is also what drew me into this space in the first place. My background was in quantitative trading, where I spent time at Tower Research and Citadel, and that shaped how I looked at digital asset markets from the beginning. What stood out to me was not simply the volatility or the excitement around crypto, but how much of the market still felt unfinished.

Also Read: In digital assets, trust is the product

The infrastructure was uneven, the tools were still catching up, and there were clear gaps between how these markets operated and how more mature financial markets were expected to function. Caladan’s own public profile reflects that path, including my earlier roles and the company’s emphasis on technology-driven trading.

One early example was the Kimchi premium, when Bitcoin traded at a much higher price in Korea than in other markets. While many saw it as an unusual market event, I saw it as a sign of something more fundamental. This was going to be a market that remained highly fragmented, with significant room for better systems and stronger execution.

In the early days, that was the gap I found most interesting. The opportunity was never just about taking part in a fast-moving market. It was about helping build the technology and decision-making infrastructure that a growing market would eventually need.

Why Singapore made sense

Singapore became the right place to build that kind of company because it offered something much more valuable than excitement. It offered seriousness. In fast-moving sectors, regulation is often framed as something that slows progress down, but that has never been how I see it. A serious market needs standards, because standards are what allow trust to accumulate over time. Without them, any sector risks becoming defined by short-term momentum rather than long-term credibility.

For firms operating across proprietary trading and institutional markets, with activities subject to different regulatory frameworks across jurisdictions, Singapore remains an important base. It has the talent, connectivity and institutional depth to support innovation, but it also expects businesses to take governance and responsibility seriously. That combination matters a great deal in digital assets, where it has often been easier to generate attention than to earn confidence.

Building from  Singapore means a company cannot rely on hype alone. It has to show that it can operate responsibly, explain how it works, hold up under closer scrutiny, and be prepared to meet increasing regulatory scrutiny, which is a harder environment in some ways, but also a healthier one if the goal is to build something that lasts. 

Also Read: SBI bets on Singapore to build Asia’s digital asset corridor

What this new phase reward

As the market matures, the qualities that matter are changing with it. For years, digital assets rewarded speed, visibility and confidence, and in a looser environment, those qualities could carry a company quite far. In a more demanding market, they are no longer enough on their own. What starts to matter more is whether a business has sound systems, a clear operating model and the discipline to keep building even after the easier optimism fades.

That is why I believe this next chapter will favour businesses that were built with stronger foundations from the outset. In markets with lighter oversight, weak structures can be hidden for a while because momentum does the work of credibility.

In a more regulated one, those weaknesses become harder to ignore, and what stands out instead is whether a firm can respond well to scrutiny, operate with consistency and build with a long view in mind. In that sense, a maturing market is not something to fear. It is what allows the sector to become more useful, more dependable and ultimately more sustainable.

Also Read: Singapore crypto adoption hits new high as 61 per cent now hold digital assets

A clearer market can be a stronger one

What is happening in Singapore’s digital asset market is a raising of standards that is setting clearer expectations for participants. The market is becoming clearer about what kind of businesses it wants to host, and that is likely to reshape who lasts and who does not.

I see that as a positive development, because a maturing market should make it harder to impress with noise and easier to recognise who has built with substance. For firms operating in digital assets, that does not make Singapore less attractive. It makes it a more meaningful place to build.

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Asia is entering a decisive decade for zero waste. What was once treated as a municipal housekeeping issue is now becoming a strategic agenda for industrial competitiveness, urban resilience, energy security, and inclusive growth. For business leaders, policymakers, and innovators, the central question is no longer whether zero waste is desirable. It is how fast the region can build the systems, incentives, and institutions to make it commercially durable and socially legitimate.

The opportunity is substantial. Asia is home to some of the world’s fastest-growing cities, largest manufacturing bases, and most dynamic startup ecosystems. It also generates enormous volumes of municipal, industrial, agricultural, and packaging waste. That combination creates a paradox: the region faces severe waste pressure, yet it also holds the largest market for solutions that can turn waste into value. In this sense, zero waste is not a niche environmental aspiration. It is a platform for new industries, new jobs, and new forms of competitiveness.

Why Asia matters

The region’s importance lies in scale and heterogeneity. On one end are highly industrialised economies with advanced recycling infrastructure, strong regulation, and growing circular-economy policies. On the other end are emerging markets where waste collection remains fragmented, informal workers play a vital role, and public systems often struggle with low separation rates. Between these extremes sit countries such as Vietnam, Thailand, Indonesia, India, the Philippines, and Malaysia, where policy ambition is rising faster than execution capacity.

This diversity is not a weakness. It is an advantage if the region treats zero waste as a modular transition rather than a one-size-fits-all doctrine. That means combining city-level pilots, corporate procurement reform, startup innovation, and national policy alignment. It also means recognising that the most effective interventions are often not the most glamorous. Better sorting, cleaner material flows, reuse logistics, composting, industrial symbiosis, and digital traceability can often unlock more value than highly visible but underperforming end-of-pipe solutions.

The new logic of zero waste

Zero waste used to be framed largely as a diversion from landfill. That remains important, but it is no longer enough. The stronger logic today is value retention. Every material that is reused, repaired, remanufactured, or biologically returned to the system avoids extraction, reduces emissions, and preserves economic value. In practice, zero waste is becoming a design principle for production systems, not just a disposal strategy.

Also Read: Long-duration energy storage: Key driver for region’s net zero goals

For enterprises, this shift changes the economics of sustainability. Waste is no longer just a cost centre. It becomes a signal of inefficiency, a source of input insecurity, and in many cases, a lost profit pool. Manufacturers can save by reducing scrap, optimising inventory, and redesigning products for durability and disassembly. Retailers can reduce packaging and logistics waste. Food companies can valorise organic residues into compost, feed, or bio-based materials. Technology firms can build platforms that improve sorting, traceability, and reverse logistics.

The winning models in Asia will likely be those that make zero waste easier, cheaper, and more reliable than linear alternatives.

Where enterprise opportunity is strongest

The commercial opportunities are concentrated in sectors that generate large waste streams and face growing costs or regulatory pressure.

• Manufacturing is perhaps the most immediate opportunity. Factories can deploy lean production, digital monitoring, predictive maintenance, and closed-loop material systems to reduce defects and scrap. This is especially relevant in electronics, automotive, textiles, metals, and chemicals. Manufacturers that can document lower waste intensity also gain credibility with global buyers, who increasingly demand traceability and ESG performance.
• Food and agriculture offer another major frontier. Asia produces and consumes enormous quantities of food, yet post-harvest losses, packaging waste, and organic waste disposal remain severe. Startups can build businesses around cold-chain optimisation, food-waste recovery, composting, bio-inputs, and surplus redistribution. In many markets, the prize is not only environmental. It is food security and cost efficiency.
• Plastics and packaging remain among the most visible pain points. Reusable packaging systems, refill models, extended producer responsibility services, and advanced sorting technologies can all create viable business models. Enterprises that help brands comply with recycled-content requirements, take-back rules, or packaging reduction targets can position themselves as indispensable infrastructure providers.
• Textiles and apparel are especially important in Southeast Asia, where manufacturing density is high, and waste from cutting, dyeing, and consumer disposal is significant. Innovations in design-for-disassembly, fibre recovery, resale platforms, and textile sorting can transform what was once treated as waste into feedstock.
• Construction and demolition materials represent another overlooked opportunity. Reuse of aggregates, modular design, and material passports can substantially reduce waste while improving resource security in rapidly urbanising economies.

The startup advantage

Tech startups are likely to play an outsized role in the next phase of zero-waste development because they can solve the coordination failures that have long constrained the sector. Traditional waste systems are often fragmented, data-poor, and slow to adapt. Startups can introduce speed, visibility, and user-centred design.

The most promising startup categories include:

• Material intelligence platforms, which use data to track waste flows, contamination rates, and recovery potential.
• AI-enabled sorting and auditing tools, which help facilities improve recovery rates and reduce manual error.
• Reverse logistics platforms, which connect collection, aggregation, and resale more efficiently.
• Reuse and refill infrastructure, which digitises packaging returns and incentives.
• Industrial by-product marketplaces, which match one company’s waste stream with another’s input demand.
• Organic waste conversion businesses, which transform food and agricultural residues into compost, soil inputs, or biochemical feedstocks.

Also Read: Asia’s climate–health gold rush is just getting started

The startup opportunity is especially strong in Asia because the region combines high waste volumes with uneven service quality. Where formal systems are incomplete, entrepreneurial platforms can leapfrog legacy models. Where large corporations face compliance pressure, startups can become technology partners. And where municipalities struggle with limited budgets, digital tools can improve efficiency without requiring immediate full-scale infrastructure replacement.

What business leaders should do

For senior executives, zero waste should be managed as a strategic transformation, not a side program. The first step is to move from broad commitments to a measurable material strategy. That requires identifying the top waste streams, the most expensive losses, and the most recoverable materials across operations and supply chains. The second step is to align procurement, product design, operations, and logistics around a shared resource-efficiency agenda.

A practical corporate roadmap usually includes five moves:

• Map material flows and identify the highest-value waste streams.
• Set product and process redesign priorities.
• Build partnerships with recyclers, refillers, and logistics operators.
• Use digital tools to measure performance and verify claims.
• Link waste reduction to cost, resilience, and market access.

The smartest companies in Asia will not frame zero waste as a burden. They will present it as a way to reduce input risk, strengthen customer loyalty, improve compliance readiness, and attract capital.

The policy imperative

Private ambition cannot succeed without public architecture. Asia needs policy frameworks that reward prevention rather than only disposal. That means stronger extended producer responsibility systems, recycled-content rules, green public procurement, landfill controls, eco-design standards, and incentives for reuse infrastructure. It also means support for informal and semi-formal waste workers, who remain essential in many Asian cities and must be integrated rather than displaced.

Governments should also focus on the enabling conditions that make zero waste scalable: standardised measurement, transparent data, stable financing, and procurement that creates demand for circular goods and services. Cities can act as powerful laboratories by piloting separate collection, pay-as-you-throw systems, repair hubs, and reuse pilots. National governments can then codify what works and remove barriers to replication.

Also Read: Turning crisis into capital: Indonesia’s climate x health pivot gains global attention

The most effective policies will be those that combine regulation with market creation. In other words, they will not only restrict wasteful behaviour. They will build markets for better alternatives.

The path ahead

Zero waste in Asia will not advance through rhetoric alone. It will advance through business models that work, policies that reward better behaviour, and institutions that convert ambition into implementation. That is why the most important phrase in the next phase may not be “zero waste” itself, but “zero waste ecosystem.”

That ecosystem includes cities, factories, startups, financial institutions, universities, ministries, and communities. It also includes the forums, networks, and coalitions that connect them. The challenge is to shift from isolated pilots to systems that can scale. The opportunity is to make zero waste a source of industrial renewal, urban livability, and inclusive prosperity.

Asia has the scale, urgency, and entrepreneurial energy to lead this transition. With the right policy design, business leadership, and convening platforms, the region can do more than manage waste better. It can redefine what growth looks like in a resource-constrained century.

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Today, I’m tearing it all down. After two decades in marketing, I’m not just changing my title; I’m changing my DNA.

For years, I felt I had made it. I navigated startups from Brunei to Singapore, believing my deep understanding of strategy was a permanent safety net. I was wrong. Standing in Singapore in 2026, looking at a job market where digital marketing manager roles are being hollowed out by automation, that safety net feels like wet paper.

The Cerulean moment

There is a scene in The Devil Wears Prada where Miranda Priestly schools her assistant, Andy, on her lumpy blue sweater. Andy thinks she’s above the stuff in the room, but Miranda explains that her sweater wasn’t a random choice; it was the result of massive technical decisions and million-dollar investments. It was Cerulean.

Most marketers today are playing the role of Andy. Just like others, we use AI to write a caption or optimise a budget, and think we’re keeping up with the fashion of the times. We aren’t. We’re just consumers of a trend someone else engineered. In Singapore, if you’re just a user, you are replaceable.

The shift from sketch to supply chain

In 2026, the golden age of the creative has been replaced by the era of the operator. Think of it like fashion: the creative dreams up the sketch, but the gold rush—the real money—is in the supply chain. It’s in the manufacturing and logistics that get that Cerulean sweater onto the rack.

To stay relevant, I hope to be the Marketing Engineer who builds the engine room. I am moving away from the soft side of creative briefs and into the hard side of Agentic Orchestration—building systems that don’t just chat, but actually execute the entire runway show.

Also Read: How AI agents are quietly rewriting the growth marketing playbook

Three tips to own the runway in 2026

• Shift from content to context: Generic AI copy is fast fashion, where it is just cheap and disposable. I must learn how to use RAG (Retrieval-Augmented Generation) to feed AI with my 20 years of proprietary strategy, so it knows my personal branding instead of guessing.
  
• Be the plumber of legacy sprawl: Singapore businesses are terrified of the 2026 Model AI Governance Framework. Don’t be a prompt engineer; be the logic builder who can use platforms like n8n or Make.com to connect the messy data safely.
• Solve the double-data entry trap: The real gold is in regulated industries, which can be fintech and logistics. Start with Flowise to build and understand private agentic workflows that automate manual verification. Let us make sure the Cerulean fabric actually makes it across the border.

So, just like everyone out there, are you ready to put in the work? Share with me your thoughts. I need to know where the best gyms are. I’m looking for recommendations from the tech community, primarily in Singapore.

• Where are the engine rooms? Which labs (like those at One-North or the Sea AI CoE) are building infrastructure rather than just AI wrappers?
• Where do I learn the hard stuff? Who is teaching agentic logic and workflow automation for non-CS veterans?
• To the founders: Are you building a platform that moves beyond writing and into doing?

The runway for digital marketers has ended. The runway for marketing engineers is just beginning. Let’s stop wearing the lumpy blue sweater and start designing the Cerulean.

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Featherless AI, the open-source AI infrastructure platform that hosts more than 3,000 models, has announced a new round of investment led by Kickstart Ventures, signalling a broader shift in how enterprises think about AI ownership and infrastructure independence.

The funding is intended to accelerate the company’s core mission: making open-source AI practical and reliable at scale. Unlike subscription-based access to proprietary large language models, Featherless AI allows enterprises to build on infrastructure they own rather than one they merely rent. This is a distinction the company says is increasingly critical as AI becomes central to business operations.

“The first wave of adoption was defined by proprietary, closed-door ecosystems. We provide a neutral ground for a second phase,” the company said in a statement.

The company framed the investment as marking a turning point in the AI market. “While the first wave of adoption was defined by proprietary, closed-door ecosystems, we provide a neutral ground for a second phase where companies can own and run their own models without being tethered to a single cloud provider or a restricted tech stack.”

A central pillar of the Featherless AI strategy is hardware diversity. Through a strategic partnership with AMD, the platform ensures its catalogue of open-source models runs natively on AMD’s ROCm.

Also Read: Without governance, AI agents risk becoming enterprise chaos engines

The company says this provides a “competitive, auditable alternative to proprietary hardware systems”, giving businesses a structural cost advantage over those locked into single-vendor GPU ecosystems.

Joan Yao, a General Partner at Kickstart Ventures, highlighted the platform’s relevance to emerging markets. “Featherless is making frontier AI accessible at a fraction of the cost — and that matters enormously in markets like Southeast Asia, where the next wave of AI-native builders shouldn’t have to pay hyperscaler prices to compete,” she said. “That’s exactly the kind of infrastructure bet we want to be behind.”

Countering AI monopolies with open architecture

Beyond commercial viability, Featherless AI has positioned itself as a structural counterweight to what it describes as the danger of AI monopolies. By ensuring that state-of-the-art models remain accessible outside proprietary “walled gardens”, the platform aims to preserve developer flexibility and prevent any single company from gatekeeping the tools used to build the next generation of applications.

The company’s technical credibility rests on original research. The founding team is responsible for RWKV, a breakthrough open-source architecture developed as a challenger to the transformer models that have dominated the field since the publication of the “Attention Is All You Need” paper in 2017. RWKV offers an alternative design that the team argues is more efficient and equally capable, a claim that has attracted significant attention from the research community.

For enterprises weighing the cost and strategic risk of reliance on closed AI systems, Featherless AI is presenting itself as a third path: the performance and breadth of a managed platform, without the dependency on a proprietary provider.

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Image Credit: Featherless AI

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Software engineers with AI skills now earn between 13 per cent and 25 per cent more than their peers, marking a significant shift from just a year ago when AI capabilities had little effect on compensation, according to a new industry report.

NodeFlair, a tech career platform based in Singapore, published its Tech Salary Report 2026 this week, drawing on more than 230,000 verified salary data points across roles and markets.

The findings point to a tech labour market in which AI fluency has moved from a desirable attribute to a measurable financial advantage.

“AI fluency is no longer a nice-to-have. It’s now a salary advantage,” said Ethan Ang, founder of NodeFlair. “Just a year ago, coding with large language models still felt more experimental than transformative for many teams. In 2025, that changed quickly.”

The report also reveals a widening salary gap between junior and senior engineers. Salaries for senior, lead and manager-level roles rose 10.8 per cent or more, compared with 5.3 per cent for junior positions and just 1.7 per cent for mid-level engineers.

Also Read: Philippines’s talent deficit is becoming an economic risk

The divergence reflects growing demand for experienced engineers who can make architectural decisions, manage ambiguity and deploy AI tools to greater effect.

Ang said engineering leaders described AI as increasingly capable of handling execution tasks traditionally assigned to entry-level staff, e.g writing routine code faster and at lower cost. However, he noted that higher-order skills such as system design, trade-off analysis and navigating complex requirements remain areas where experienced engineers hold a clear edge.

At the top of the market, the highest-earning 10 per cent of engineers saw salary increases of up to 19 per cent, further widening the gap between top performers and the broader workforce.

What changed in 2025

NodeFlair attributed the turnaround to two converging factors: the maturation of AI coding tools into production-grade workflows, and a shift in how employers assess technical talent.

In 2024, many companies were still running pilots, and the productivity case for AI remained unclear. By 2025, tools enabling agentic coding workflows had become widely adopted, making the return on AI investment more tangible and prompting companies to price AI skills accordingly.

For early-career engineers, Ang urged embracing AI rather than treating it as a competitive threat. He noted that, on the ground, younger engineers have been quicker to adopt AI tools than their senior counterparts, and that pairing those skills with strong fundamentals in problem-solving and system design remains the most durable path to career value.

Also Read: What happens when AI starts talking to AI at work

The next wave

Looking ahead, NodeFlair expects the largest salary premiums to accrue not to those holding AI-specific job titles, but to professionals who combine domain expertise with practical AI execution — product managers who can prototype with AI, data professionals who can move AI models into production, and engineers who can work fluidly alongside AI agents.

“The biggest premiums will go to people who can combine domain expertise with AI execution,” Ang said. “Not just knowing the tools, but knowing how to apply them to create measurable business value.”

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Image Credit: NodeFlair

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