People used to ask AI for help with parts of modern life, like making an email sound less annoyed or explaining a spreadsheet formula without forcing anyone to revisit their relationship with mathematics. Lately, the exchange has become more intimate. The same tools built to summarise, draft and optimise are now being invited into moments of doubt, stress and loneliness.

This shift is global, but Singapore gives us a useful early signal. In my work with individuals and organisations in Singapore, I often see how much emotional load people carry while still functioning. Many keep moving through demanding workdays, family responsibilities and social expectations while privately trying to make sense of what they feel.

AI is starting to enter that private space.

A recent Singapore-based study offers a useful glimpse of how this is already happening. In 2026, researchers explored how foreign domestic workers in Singapore used a large language model chatbot while managing caregiving burden. The findings were cautious, but revealing. Participants described the chatbot as emotionally validating, psychologically safe, linguistically accessible, and useful for reassurance and companionship.

For a startup audience, this should raise more than a social welfare eyebrow.

The study points to a wider behaviour change that founders, product teams and employers need to understand. People are beginning to use general-purpose AI tools for emotional processing, especially when human support feels too slow, expensive, risky, or socially complicated. The user may begin by asking for help with a message. Within a few minutes, they may be asking whether their reaction makes sense, how to handle a difficult conversation, or why they feel so depleted.

That is where product design crosses into psychology and the appeal is clear. An AI can respond in plain language, adapt to imperfect phrasing, and give people a feeling of being heard without the social exposure that often comes with disclosure. For people under pressure, that can be powerful.

Also Read: How centralised exchanges swapped crypto ethos for Wall Street fees: Why this will fail

A 2026 cross-cultural study of more than 4,600 participants across seven countries found that people are already using large language models as always-available, non-judgmental confidants for emotional support. The prompts collected in that study showed people seeking help for loneliness, stress, relationship conflict and mental health struggles. This is no longer a fringe use case for companion apps. It is becoming part of ordinary interaction with general-purpose AI.

That shift has real commercial relevance.

If people are using AI tools to manage emotional load, then workplace software, productivity platforms, coaching apps, HR tools and digital health products are already operating closer to mental health territory than many companies may realise. A product designed to help someone draft a message can quickly become a place where they disclose fear, resentment, shame or distress. A tool designed to improve productivity can become the place where an employee admits they are no longer coping.

This creates opportunity, but also responsibility.

Emotionally responsive AI can reduce friction. It can help people name what they are experiencing, organise their thoughts and access support earlier. In a place like Singapore, where people may be managing long hours, family responsibilities, cultural expectations and pressure to remain composed, a low-barrier tool can feel useful. For employers and founders, that usefulness is exactly why the ethical design questions cannot be left until later.

Singapore gives this global shift a sharper local frame. In April 2026, NTU Singapore and NHG Health announced ASPIRE, Singapore’s first work-study training pathway for clinical psychology. The announcement pointed to a clear pressure point: demand for mental health support is rising, while the human workforce takes time to build. That is the gap AI is already moving into.

There is also a trust issue here.

People disclose differently when they believe no human is listening. They may share sensitive details with AI because the interaction feels contained, even when the data environment is more complex than it appears. For companies building emotionally fluent products, privacy cannot sit buried in compliance language. It has to be visible in the user experience. People need to understand what they are sharing, where it goes, how it may be used, and what the tool can do when distress escalates.

Also Read: The death of the traditional org chart: How AI is reshaping work

The most important lesson for startups is that emotional support may appear inside products that were never designed for mental health. A person may stumble into it while drafting a resignation email, preparing for a performance review, translating a difficult message, or trying to make sense of workplace tension. The product team may think they are building a writing assistant. The user may experience it as the first place they can say what they are really feeling.

That is where the next stage of AI design needs more psychological literacy.

Emotionally responsive tools should help people reflect, clarify and access support earlier. They should also make their limits clear. When a user starts disclosing distress, the product needs thoughtful guardrails: clear privacy language, careful emotional tone, referral pathways, escalation options and design choices that encourage agency rather than dependence.

Singapore’s 2026 research gives us an early signal of where this is heading. The study focused on foreign domestic workers using an AI chatbot for caregiving burden, but the lesson reaches further than that setting. People are turning to AI because it is immediate, private and easier to approach than many human systems of support.

For founders and organisations, the takeaway is simple: once a product becomes emotionally useful, it carries emotional responsibility.

AI is no longer only answering prompts. It is becoming part of how people process pressure, uncertainty and loneliness. The companies that understand this early will design tools that earn trust, protect users and know when to guide people back towards human support.

That is the next frontier of AI emotional support. The question is no longer whether people will bring their distress into the interface. They already are. The real design challenge is what the interface does with it.

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MAKATI CITY, Philippines — Sprout Solutions, the Philippines’ leading homegrown HR and payroll technology company, will hold the State of HR Summit 2026 on July 8, 2026, at Dusit Thani Manila, bringing together HR leaders, business executives, and people managers to discuss how organisations can accelerate performance with AI while remaining people-first.

Presented in partnership with workplace consultancy BS Works, this year’s summit carries the theme Leading a People-First AI Transformation and will feature the exclusive launch of the State of HR 2026 Report, based on findings from a nationwide survey of 3,516 Filipino professionals.

As AI becomes increasingly embedded in business operations, organisations face a new challenge: ensuring technological transformation strengthens employee trust, engagement, and readiness rather than undermining them. The summit will focus on how leaders can navigate this shift by aligning people, processes, and technology.

The discussion is grounded in findings from Sprout and BS Works’ nationwide AI and Your Evolving Workplace survey, conducted in May 2026 through Sprout’s first AI-powered survey application.

Early results reveal that 95 per cent of Filipino professionals are familiar with AI, while 89 per cent welcome AI-related changes in the workplace. However, organisational readiness appears to lag behind employee enthusiasm. Only 35 per cent of respondents report that their employers currently provide AI-related upskilling opportunities.

From AI adoption to AI leadership

The summit will open with remarks from Patrick Gentry, CEO and Co-Founder of Sprout Solutions. Cliff Eala, Managing Partner of BS Works, will deliver the keynote presentation and formally launch the State of HR 2026 Report, sharing insights on how employees are adapting to AI-driven workplace change.

Throughout the day, executive panels and discussions will explore the growing need to align HR, operations, and compliance functions, and the leadership capabilities required to guide organisations through rapid technological change. 

Among a host of industry leaders joining the summit are Maria Lourdes Ann “L.A.” Cruz, Vice President for People at Lufthansa Technik Philippines; Alvanson So, former HR leader at Canva; Eli Harell, Co-Founder of EmergePH; and Atty. Arlene De Castro, Founder and Managing Partner of De Castro Law Firm and De Castro Consulting. 

Attendees will also receive a preview of Sprout’s product roadmap, highlighting how the company’s people-first AI platform aims to connect payroll, compliance, performance, and workforce data within a unified ecosystem.

The readiness gap behind AI optimism

Additional survey findings highlight the urgency of the conversation. While 84 per cent of respondents report that their organisations already use AI and 83 per cent say AI helps them work more efficiently, nearly 45 per cent feel overwhelmed by the pace of workplace change.

Also Read: AI transformation starts with people, not platforms

“Filipinos have embraced AI faster than many organisations anticipated. The challenge now is helping leaders close the gap between adoption and readiness by building trust, developing skills, and creating workplaces where people can thrive alongside technology,” said Kislay Chandra, Chief Operations Officer, Sprout Solutions.

The event’s sponsors include mWell by Metro Pacific Health Tech as Platinum Sponsor, Hubstaff and MetroMart Philippines as Gold Sponsors, and Hive Health as Silver Sponsor. Supporting the initiative as Strategic Partners are Philippine Society for Talent Development, Mind You, and Plan International Pilipinas, while Rappler, Manila Standard, The Manila Times, Vritimes, TechShake, and Malaya Business Insight serve as Media Partners.

Recognising outstanding organisations through the foresight awards

The State of HR Summit 2026 will also host the inaugural Sprout Foresight Awards, recognising organisations demonstrating workforce health and people-first workplace practices.

Major awardees will be honoured through the Great Employer Awards, alongside other recognitions that celebrate excellence across key areas of organisational health. Through the Foresight Awards, Sprout aims to spotlight companies that are helping shape healthier, more resilient, and future-ready workplaces in the Philippines.

Powered by Foresight, Sprout’s new AI-powered predictive and prescriptive HR analytics solution, the awards reflect Sprout’s commitment to helping organisations better understand their workforce through meaningful data and actionable insights. 

Secure your seat at State of HR 2026

Registration for the State of HR Summit 2026 is now open at sohr.sprout.ph. Individual and table packages are available, and interested participants may visit the website for current rates and event details.

About the survey

The AI and Your Evolving Workplace survey was conducted by Sprout Solutions in partnership with BS Works and fielded nationwide in May 2026 through Sprout’s first AI-powered survey application. The study gathered responses from 3,516 professionals across major Philippine industries, including information and communication, business process outsourcing, finance, retail, healthcare, and manufacturing, representing multiple generations in the workforce.

About Sprout Solutions

Sprout Solutions is the people-first AI platform for payroll, compliance, and work, serving businesses across the Philippines and Southeast Asia. Founded in 2015, Sprout combines AI-powered payroll, HR, recruitment, performance management, employee engagement, and financial wellness solutions into one intelligent platform designed to help organisations operate more efficiently while supporting their people at scale.

Trusted by more than 2,000 companies and 350,000 users, Sprout complements its technology with managed services, implementation expertise, and dedicated customer success teams.

Sprout exists to impact the lives of employees by improving businesses in the communities it serves.

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Few ideas in product management have travelled as widely, and done as much quiet damage, as the line that the product manager is the CEO of the product.

It sounds flattering. It sounds decisive. It gives the role a sense of weight and status that many product managers, especially early in their careers, are understandably drawn to. It also gives founders and executives a convenient shorthand. Say it once, and everyone feels they understand what product management is supposed to be.

The trouble is that they usually understand the wrong thing.

The phrase survives because it gives emotional clarity in a role that is often structurally ambiguous. Product managers sit in the middle of competing demands, partial authority, and shifting expectations. Telling them they are the CEO of the product feels empowering. It appears to solve the identity problem. It suggests ownership, leadership, and accountability in one neat line.

But neat lines are often expensive in real organisations.

Product leadership is not executive sovereignty

The first issue is simple. A CEO has formal authority. A product manager usually does not.

That is not a trivial difference. It is the difference.

A CEO can allocate capital, set structure, hire leaders, make final trade-offs across functions, and carry formal accountability for enterprise outcomes. A PM does not sit in that position. A PM works through influence, judgement, framing, trust, and alignment across people who often report elsewhere and carry valid goals of their own.

Pretending those are the same kind of leadership is not empowering. It is misleading.

In fact, one of the core disciplines of strong product management is learning how to lead without the fantasy of unilateral control. That is not a lesser form of leadership. In many ways, it is a more demanding one. It requires a PM to earn movement through clarity and substance rather than title. It requires them to understand technical constraints, business context, customer reality, and organisational incentives well enough to help a group arrive at better decisions together.

That is real leadership. It just is not CEO leadership.

The analogy encourages PMs to reach for authority they do not actually hold, instead of helping them master the kind of authority the role genuinely requires.

Also Read: The systemic minimum effective dose: Redesigning productivity through precision

The myth creates bad PM behaviour

Once people internalise the CEO idea, their behaviour often shifts in subtle but damaging ways.

Some start treating every decision as if the product should be the final arbiter. They become overly attached to control. They mistake coordination for command. They expect engineering, design, data, and go-to-market teams to line up behind product as though product were the natural centre of gravity in every discussion.

That posture creates tension quickly.

Engineering starts feeling managed rather than partnered with. Design feels invited in after the real thinking is done. Commercial teams learn that the product wants accountability without always carrying enough of the customer and market burden. The PM, meanwhile, often becomes more performative than effective. They begin signalling certainty, weight, and strategic dominance when what the situation actually needs is sharper listening, better synthesis, and more honest trade-offs.

It also creates bad organisational expectations

The damage is not limited to PMs themselves. The phrase also teaches the rest of the company to expect the wrong things from the product.

Executives start assuming PMs can simply make difficult trade-offs happen, even when the underlying functions are misaligned. Founders expect product managers to absorb accountability for outcomes without giving them enough organisational leverage to shape those outcomes properly. Engineers begin to resent the product for behaving like management without carrying equivalent depth in technical delivery. Customer-facing teams assume PMs should absorb every strategic tension because the role has been framed as the owner of the whole thing.

This creates a peculiar trap.

The PM is treated as highly accountable, but not always meaningfully empowered. They are expected to think like a general manager, influence like a founder, decide like an executive, and still somehow remain collaborative, humble, data informed, customer centric, and delivery conscious.

Also Read: People don’t want productivity hacks anymore, they want sustainable ways to live

The phrase flatters the product and diminishes everyone else

There is another problem with the CEO analogy that product people do not always say out loud. It quietly reduces the contribution of other disciplines.

If the PM is the CEO of the product, what exactly does that make the engineering lead, the designer, the data lead, the researcher, or the operational teams who deal with adoption reality every day? Supporting cast. Functional experts. Advisers to the central brain.

That is not how good products are built.

Strong products emerge from serious cross-functional thinking, where each discipline shapes the outcome in material ways. Engineering does not merely execute a product idea. It often determines what is elegant, resilient, scalable, and even strategically possible. Design is not visual packaging around a product direction. It shapes behaviour, trust, comprehension, and flow. Research does not just validate. It often reveals that the original framing was weak. Commercial teams do not only distribute value. They expose whether the product meets reality outside internal narratives.

The role is closer to an integrator than a sovereign

If the CEO analogy is wrong, what is a better frame?

In my view, a PM is far closer to an integrator of decision quality than a sovereign owner of the product.

That may sound less glamorous, but it is actually more accurate and, in mature organisations, more powerful.

A good PM helps the company make better product decisions by integrating customer truth, business judgement, delivery reality, and strategic intent. They create coherence where functions would otherwise optimise locally. They help teams decide what matters, what trade-offs are real, what assumptions are weak, and what evidence should change the course.

That is not a small role. In fact, it is a very consequential one.

Also Read: When execution is free, the brief becomes the product

Why younger PMs are particularly harmed by this myth

The CEO phrase does particular damage early in a PM’s career because it teaches the wrong aspiration.

Instead of learning how to ask better questions, build trust across functions, reason through trade offs, and develop genuine taste in product judgement, many younger PMs end up performing seniority. They overfocus on status, decisiveness, and visible ownership. They try to sound like mini executives before they have learned how to become truly useful in complex product environments.

This leads to a familiar pattern. The PM talks strategy when the team needs clarity. They push for alignment without understanding why disagreement exists. They seek authority before they have built enough credibility. They chase the optics of leadership rather than the substance of it.

The irony is that the best PMs often look less like product CEOs and more like unusually effective interpreters of complexity. They are calm under ambiguity. They know when to push and when to absorb. They improve decision quality without needing to dominate every room. They understand that influence is not diluted by collaboration. It is often made stronger by it.

Those are harder lessons to learn if the profession keeps telling people the goal is to act like a CEO.

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Singapore-based dConstruct Technologies has closed a US$125 million Series A round, giving the city-state’s robotics sector one of its more visible funding wins at a time when deeptech companies across Southeast Asia face tougher scrutiny on commercial traction.

The National Robotics Programme of Singapore announced the financing on Thursday, positioning it as the standout outcome from the inaugural cohort of RoboNexus, its venture-building accelerator. The programme also highlighted two other graduates, LionsBot and Spinoff Robotics, as examples of Singapore robotics companies moving from research and pilot deployments into international markets.

Also Read: Thailand’s Amity Robotics raises US$7M to take physical AI from malls to global markets

The funding round places dConstruct among a relatively small group of regional robotics companies able to raise large institutional cheques. The company did not disclose the investors in the round, the valuation, or the breakdown between primary and secondary capital.

That lack of detail matters. Robotics companies tend to be capital-intensive, with long development cycles, massive hardware costs, integration work, and slower enterprise procurement timelines. Still, the size of the round suggests that investors are willing to back robotics platforms that can show deployment potential beyond narrow proof-of-concept projects.

Building for messy, indoor environments

dConstruct’s core technology is d.ASH, a proprietary suite that integrates 3D scanning and perception technologies to enable autonomous robots to operate in complex, GPS-denied environments.

That focus is commercially significant. Many real-world robotics applications inside buildings, tunnels, transport hubs, industrial sites, and underground infrastructure cannot rely on GPS. Robots operating in these settings need localisation, mapping, obstacle avoidance, and data capture systems that can function in cluttered, changing environments.

The company is targeting use cases across the built environment, security, inspection, logistics, and entertainment. Its disclosed customers and partners include Boustead Projects, Singapore’s Defence Science and Technology Agency, JRE Ventures of the East Japan Railway Company Group, SBS Transit, and SoftBank Robotics Singapore.

Chinn Lim, CEO of dConstruct Technologies, said RoboNexus had helped the company access industry partners and deployment opportunities, while the funding milestone reflected confidence in its plan to build “reality capture and robot automation solutions from Singapore to the world”.

The phrase is ambitious, but the underlying market is real. The International Federation of Robotics said global sales of professional service robots grew 30 per cent in 2023, with logistics, cleaning, inspection, and hospitality among key categories. Asia remains the centre of gravity for robotics adoption: the federation’s industrial robot data shows Asia accounted for about 70 per cent of new industrial robot installations globally.

Also Read: German-listed DDB acquires Singapore’s Infinium Robotics in US$24M share deal

Singapore has an additional advantage. Its small labour pool, high labour costs, ageing workforce, and dense urban infrastructure make it a natural testbed for automation. The country also ranks among the world’s most robot-dense economies, with the International Federation of Robotics placing Singapore near the top globally for industrial robot density.

A new robotics base in Punggol

dConstruct is also establishing a 42,000-square-metre global headquarters at Punggol Digital District. The facility, named dC Plus, is expected to be completed by the end of 2026.

The site will include robotics testing environments for wheeled, quadruped, and humanoid platforms, as well as collaborative workspaces and community programmes. The location is notable: Punggol Digital District has been positioned by Singapore as a hub for digital economy firms, applied research, and industry-academia collaboration.

For dConstruct, the headquarters could help address one of the sector’s persistent bottlenecks: moving from controlled demonstrations to repeatable deployment in real-world settings. Robotics companies often struggle not because their systems fail in the lab, but because customers need machines that can operate safely and reliably across unpredictable sites.

That challenge is especially acute in Southeast Asia, where built environments vary widely across markets. A robot that performs well in Singapore’s regulated commercial facilities may face different conditions in Indonesia’s logistics sites, Thailand’s factories, Vietnam’s industrial parks, or the Philippines’ mixed-use developments.

RoboNexus’s broader cohort

The National Robotics Programme also pointed to LionsBot and Spinoff Robotics as evidence that the RoboNexus model can support companies beyond early research commercialisation.

LionsBot, a Singapore-based cleaning robotics company, is now present in more than 40 countries and has overseas subsidiaries in Dallas, Amsterdam, and Chennai. The company says it has deployed more than 6,000 robots globally. Its expansion has been supported by a collaboration with WISAG, a European facility management group, which contributed to the development of LionsBot’s R5 cleaning robot launched in April 2026.

Cleaning robots are among the more mature categories in professional service robotics, but competition is intense. LionsBot faces global and regional rivals including Gaussian Robotics, SoftBank Robotics, Pudu Robotics, and Singapore-based players such as Sesto Robotics and OTSAW in adjacent automation segments. The differentiator is less about whether a robot can clean a floor and more about fleet management, maintenance economics, procurement relationships, and customer support across markets.

Dylan Ng, CEO and co-founder of LionsBot, said the company intends to support newer RoboNexus cohorts by sharing lessons from R&D, product development, commercialisation, and international expansion. That is useful if it moves beyond founder storytelling and into practical guidance on manufacturing, compliance, servicing, and channel partnerships.

Spinoff Robotics, meanwhile, has taken a different route. The company was acquired by Nanoveu Limited, an Australian Securities Exchange-listed technology company, giving it access to public-market capital, commercial platforms, and international growth channels.

The company was established to commercialise tethered aerial robotics technology for cleaning and inspection of hard-to-reach infrastructure. Its applications include urban infrastructure, facilities management, and industrial inspection. The acquisition also allows Spinoff Robotics to expand its licensed Singapore University of Technology and Design platform into non-tethered drone applications.

Also Read: AMC Robotics to build US$3.5M Vietnam factory as SEA automation race heats up

Tan Chee How, co-founder of Spinoff Robotics, said the National Robotics Programme helped the company move from research into commercialisation by opening access to industry partnerships and deployment opportunities.

Why this matters for Southeast Asia

For Southeast Asia, the story is not simply that a Singapore startup has raised a large round. The more important question is whether robotics companies in the region can build commercially durable businesses rather than remain grant-supported engineering projects.

Demand is not the problem. Southeast Asia’s factories, airports, ports, hospitals, malls, construction sites, and transport operators all face labour, safety, and productivity pressures. Governments across the region are also pushing automation through industrial upgrading programmes, including Singapore’s Advanced Manufacturing initiatives, Malaysia’s Industry4WRD policy, and Thailand’s Eastern Economic Corridor strategy.

The harder issue is deployment economics. Robotics companies must prove that their systems reduce labour dependency, improve safety, or generate operational savings large enough to justify upfront costs, integration work, and ongoing maintenance. Customers are no longer impressed by pilot projects alone.

dConstruct’s US$125 million Series A gives it the capital to build for that test. RoboNexus gives Singapore a stronger narrative around robotics venture-building. But the next stage will be less about national ambition and more about commercial evidence: repeat customers, overseas revenue, functioning fleets, and technology that survives outside carefully managed demo environments.

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Omoway arrives in Southeast Asia with a compelling origin story: BYD co-founder as an investor, a fresh funding round, and a market that looks, on paper, ripe for disruption. Two-wheelers dominate urban mobility across the region. Fuel costs sting. Air quality in cities like Jakarta and Ho Chi Minh City has made clean transport a political talking point. The conditions seem ideal.

But the electric motorcycle race in SEA has already claimed confident entrants. Gojek-backed Electrum has struggled to scale beyond Indonesia. Vmoto, Gogoro, and a clutch of domestic players are all competing for the same cost-sensitive, range-anxious rider. The graveyard of well-funded EV two-wheeler startups that underestimated after-sales infrastructure, battery swap logistics, and the sheer informality of SEA’s last-mile ecosystem is getting crowded.

Omoway’s BYD connection gives it credibility and potentially a supply chain edge. But credibility does not equal distribution, and supply chain advantages dissolve quickly when a competitor has deeper dealer networks or better financing schemes for first-time buyers.

The funding is a starting gun, not a finishing line. Execution in SEA’s motorcycle market is where most stories end, not begin.

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REGIONAL

Grab completes US$425M acquisition of Stash Financial: The acquisition of the Singapore-based wealth management app deepens Grab’s financial services push, giving it a regulated investment platform and expanding its superapp ambitions beyond payments and lending.

Nadiem Makarim sentenced to 10 years in Chromebook case: Indonesia’s former education minister and Gojek co-founder was convicted of corruption linked to a government Chromebook procurement programme, marking one of SEA’s most high-profile founder-turned-official prosecutions.

TikTok reportedly cut 450 Tokopedia tech jobs: ByteDance-owned TikTok has laid off nearly 450 technology staff at Tokopedia, signalling a sharp consolidation of the Indonesian e-commerce operation following its merger with TikTok Shop.

Alibaba payment firm pays US$600M to resolve US probe: Ant International, Alibaba’s payments affiliate with significant SEA operations, agreed to pay US$600M to settle a US regulatory investigation, in one of the largest fintech enforcement actions involving an Asian firm.

German-listed DDB acquires Infinium Robotics for US$24M: Singapore drone logistics firm Infinium Robotics was acquired by Frankfurt-listed DDB in an all-share deal, marking a rare cross-border public market exit for a SEA deep-tech startup.

Etaily lands Vynn Capital investment for regional push: E-commerce enabler Etaily secured fresh funding from Vynn Capital to accelerate its expansion across Malaysia, Singapore, and Indonesia, targeting brand commercialisation across SEA’s fragmented retail landscape.

BYD co-founder-backed Omoway raises to enter SEA EV race: Electric motorcycle startup Omoway, backed by a BYD co-founder, secured funding to enter SEA’s competitive two-wheeler EV market, where infrastructure gaps and entrenched incumbents remain formidable barriers.

Qashier raises US$6M as SEA SME payments market heats up: Singapore-based point-of-sale startup Qashier, which is profitable, raised US$6M to expand its payments and business management platform across SEA’s underserved SME segment.

Thailand’s Amity Robotics raises US$7M for global push: The Bangkok-based physical AI startup secured US$7M to scale its autonomous service robots beyond malls into global commercial environments, betting on embodied AI as the next hardware frontier.

LinqAlpha raises US$22M to bring agentic AI to investors: Singapore-based LinqAlpha secured US$22M to deploy AI agents that assist public market investors with research and decision-making, targeting institutional and professional investor workflows.

Acti raises US$5.3M to build AI agent layer on keyboards: Singapore startup Acti secured US$5.3M to embed AI agents directly into keyboard-level interactions, positioning the input device as an ambient intelligence layer across enterprise workflows.

AnyMind opens 20 live commerce studios in Indonesia: Tokyo-listed AnyMind is expanding its live commerce infrastructure in Indonesia with 20 new studios, doubling down on Southeast Asia’s fastest-growing social selling market.

SG robotics accelerator cohort raises US$125M, logs exit: Singapore’s robotics accelerator programme produced a cohort that collectively raised US$125M and recorded at least one exit, underscoring the city-state’s growing traction as a deep-tech startup launchpad.

Akro AI raises US$700K to automate regulated data workflows: Singapore-based Akro AI closed a pre-seed round of US$700K to build AI-powered data automation tools targeting compliance-heavy industries such as finance and healthcare.

TransTrack embeds AI across products and operations: Indonesian fleet management platform TransTrack detailed how it is integrating AI across its core product stack and internal operations, reflecting a broader shift among SEA SaaS firms from AI experimentation to deployment.

73% of SEA employees say leadership is digitally disconnected: A Lark survey found nearly three-quarters of Southeast Asian employees believe their leaders lack understanding of real digital transformation needs, exposing a widening gap between C-suite strategy and ground-level execution.

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INTERVIEWS & FEATURES

Vietnam’s healthtech boom has a talent problem: Rapid growth in Vietnam’s health technology sector is undermined by a chronic shortage of professionals who combine clinical knowledge with technical skills — a constraint that few investors or founders are openly addressing.

VCs writing off Indonesia risk a US$300B mistake: A sharp opinion piece argues that investor pessimism about Indonesia’s startup market is structurally misguided, pointing to domestic consumption, demographic tailwinds, and underserved digital infrastructure as underpriced opportunities.

SEA enterprise fitness tech still has homework to do: As Southeast Asia’s corporate wellness market grows, enterprise fitness platforms struggle with fragmented employer demand, inconsistent engagement, and a benefits culture that has not yet normalised digital health tools.

Qualcomm selects 15 APAC startups for AI innovators programme: Qualcomm named 15 Asia-Pacific startups for its 2026 AI Program for Innovators, offering hardware access, technical mentorship, and go-to-market support to early-stage AI and edge computing companies.

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INTERNATIONAL

Microsoft launches AI deployment company with US$2.5B commitment: Microsoft has established a dedicated AI deployment unit backed by US$2.5B, a move that positions the company as a direct competitor to systems integrators and managed service providers in enterprise AI rollout.

SoftBank launches AI cloud unit targeting 10-gigawatt capacity: SoftBank’s new AI cloud division plans to tap 10 gigawatts of compute capacity, signalling a massive infrastructure bet that will shape AI availability and pricing across Asian markets including SEA.

Anthropic launches Claude Sonnet 5 for cheaper agent runs: Anthropic’s Claude Sonnet 5 lowers the cost of running AI agents, a development with direct implications for SEA startups building agentic applications on third-party model infrastructure.

Anthropic eyes Samsung for AI chip production: Anthropic is in discussions with Samsung to produce custom AI chips, a potential shift away from TSMC that could reshape semiconductor supply chains across Northeast and Southeast Asia.

OpenAI proposes US government take 5% stake: OpenAI has proposed that the US government acquire a 5% equity stake in the company as it restructures into a for-profit entity, a move designed to neutralise political opposition while deepening state alignment.

Alibaba merges enterprise AI tools to battle Tencent: Alibaba is consolidating its enterprise AI product suite into a unified platform to sharpen competition with Tencent, intensifying China’s cloud and AI wars with implications for SEA enterprise software buyers.

Zuckerberg tells staff AI agents are behind schedule: In an internal address, Meta’s CEO acknowledged that AI agent development has not advanced as quickly as anticipated, a rare admission that tempers expectations around autonomous AI systems across the industry.

Indian tycoon bets US$30M on AI alternative to MS Office: An Indian tech billionaire has committed US$30M to build an AI-native productivity suite designed to compete with Microsoft Office, targeting the vast price-sensitive enterprise market across South and Southeast Asia.

Oriental Semiconductor plans US$212M fundraise: Chinese chipmaker Oriental Semiconductor is planning to raise US$212M, reflecting sustained capital appetite in Asia’s semiconductor sector amid ongoing supply chain restructuring and US export restrictions.

Meta quietly launches vibe-coded gaming app Pocket: Meta’s Pocket app, built using AI-assisted “vibe coding”, marks the company’s latest attempt to capture younger mobile audiences, with potential implications for SEA’s large gaming and social media user base.

UK investors sue Binance and CZ for US$200M: A group of UK-based investors has filed suitagainst Binance and its former CEO Changpeng Zhao over losses from high-risk crypto derivatives products, in one of the largest investor actions against the exchange.

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CYBERSECURITY

Anthropic’s Fable model frustrates security researchers: Guardrails on the public cybersecurity model are too blunt, researchers say — blocking routine tasks like reading blog posts or writing secure code by triggering keyword-based filters rather than assessing actual risk or intent.

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SEMICONDUCTOR

IQM, Europe’s first public quantum firm, flags uncertain future: Finnish quantum computing company IQM, the continent’s first publicly listed quantum firm, has acknowledged deep uncertainty about the technology’s commercial timeline, a sobering signal for investors backing quantum plays across Asia.

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AI

The next AI winners in SEA won’t be AI companies: A sharp analysis argues that the real beneficiaries of AI adoption in Southeast Asia will be sector-specific operators — logistics, healthcare, fintech — that embed AI into existing workflows rather than pure-play AI firms.

The AI layoff trap points straight at Southeast Asia: SEA’s export-driven manufacturing and services economies face disproportionate exposure to AI-driven job displacement, with mid-skill white-collar roles in BPO, shared services, and data processing most immediately at risk.

The accordion effect: how AI expands and compresses work: A framework piece argues that AI alternately expands and compresses the scope of human work in cycles, with implications for how organisations plan headcount, upskilling, and productivity measurement.

The rise of AI twins: from assistant to infrastructure: AI digital twins are evolving from simple productivity tools into embedded organisational infrastructure, shifting how enterprises think about knowledge management, decision support, and workforce continuity.

The death of the traditional org chart via AI: AI is fundamentally restructuring how organisations are designed, making hierarchical org charts obsolete and pushing companies toward fluid, task-oriented team structures built around AI-human collaboration.

Singapore AI and the rise of emotional outsourcing: As Singapore accelerates AI adoption, a commentary examines the growing tendency to delegate emotional and relational tasks — mentorship, feedback, even empathy — to AI systems, and what that costs organisations long-term.

AI slop is a strategy problem, not a content problem: Low-quality AI-generated content, so-called “AI slop“, is proliferating not because tools are inadequate but because organisations lack clear content strategy, editorial standards, and accountability structures.

Why hiring AI experts is the most common startup mistake: Most startups hiring AI specialists in 2026 are solving the wrong problem — the bottleneck is rarely talent, more often unclear use cases, poor data infrastructure, and leadership that has not defined what AI should actually do.

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THOUGHT LEADERSHIP

Why compute futures make sense in a deflationary market: As AI compute costs fall, a contrarian case argues that futures contracts on compute capacity remain a rational hedge, because demand volatility, not price direction, is the real risk for AI-dependent businesses.

The extreme fear metric and forced liquidation bounces: A market analysis argues that the current crypto market recovery is driven not by renewed conviction but by the mechanical unwinding of forced liquidations — a distinction that matters for anyone reading the bounce as a sentiment shift.

Fringe benefits: disruption starts with unwanted customers: Drawing on Clayton Christensen’s disruptive innovation theory, this piece argues that the most durable startups begin by serving customers incumbents actively ignore and that founders who chase validation from day one misread where markets break open.

What C-dramas teach founders about market entry: Using the global spread of Chinese television dramas as a case study, this commentary draws lessons for SEA founders on cultural localisation, platform leverage, and the sequencing of international expansion.

In Singapore, founders win on foresight, not nerve: A perspective piece challenges the mythos of the bold, risk-taking founder, arguing that Singapore’s most successful entrepreneurs succeed through structured thinking, regulatory navigation, and long-horizon planning rather than bravado.

How centralised crypto exchanges adopted Wall Street’s worst habits: A critical essay argues that CEXs have abandoned crypto’s founding ethos by replicating opaque fee structures, preferential access tiers, and rent-seeking behaviours — and that this will ultimately drive users toward decentralised alternatives.

Everyone told me to write for humans, but they can’t find my page: A practitioner’s essay on the tension between SEO-driven content and human-first writing, arguing that discoverability and readability are not opposites but that most content teams treat them as if they are.

The post Ecosystem Roundup: Why Omoway’s real test hasn’t started yet appeared first on e27.

Southeast Asia’s technology ecosystem has staged a dramatic comeback in the first half of 2026, with total venture funding hitting US$7.4 billion, a staggering 130 per cent jump compared to the US$3.2 billion raised in H1 2025, and a 122 per cent rise over the US$3.3 billion recorded in H2 2025.

The figures, drawn from Tracxn’s Geo Semi Annual Report for SEA Tech H1 2026, paint a picture of a region that has shaken off investor caution and is now pulling serious capital at a pace not seen in years.

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

The resurgence is being driven by a handful of mega-rounds, a red-hot enterprise infrastructure segment, and Singapore’s increasingly iron grip on the region’s funding flows.

The mega-round effect

If there is one story that defines H1 2026, it is the outsized influence of blockbuster funding rounds. The period saw 12 funding rounds of US$100 million or more, triple the four such rounds recorded in H2 2025 and double the six seen in H1 2025.

Leading the charge is DayOne, a Singapore-based data centre company founded in 2022, which closed two tranches of a Series C round totalling a jaw-dropping US$4.5 billion, comprising a US$2.5 billion close in June and a US$2 billion close in January. Backers include Hillhouse, Coatue, and the Indonesia Investment Authority. DayOne’s cumulative funding now stands at US$6.4 billion, making it arguably the most heavily capitalised startup in the region.

Behind it, open-source database platform Supabase raised US$500 million in a Series F round backed by GIC, Accel, and Craft Ventures, while cross-border payments giant Airwallex secured US$320 million in a Series H round, pushing its valuation to US$11 billion, with T. Rowe Price, Baillie Gifford, and QED Investors among those writing cheques.

Other notable rounds include AI video generation platform PixVerse (US$300 million, Series C), AI infrastructure firm SiliconFlow (US$294 million, Series B), and semiconductor packaging company Silicon Box (US$150 million, Series C).

Enterprise infrastructure is the undisputed king

The sectoral breakdown reveals an unmistakable shift towards deep-tech and infrastructure plays. According to Tracxn, Enterprise Infrastructure was the top-performing sector in H1 2026, pulling in US$5.2 billion, a 260 per cent increase from the US$1.5 billion raised in H2 2025, and a remarkable 503 per cent surge compared to the US$870 million raised in H1 2025. DayOne’s twin raises account for the bulk of this, but the appetite for data centre and AI infrastructure assets is clearly structural, not incidental.

Enterprise Applications came in second with US$2 billion raised, up 126 per cent from US$888 million in H2 2025 and 202 per cent from US$666 million in H1 2025. Fintech, by contrast, slipped slightly to US$685 million, down 3 per cent from US$706 million in H2 2025 and 4 per cent from US$711 million in H1 2025, a sign that the segment, long the darling of SEA investors, may be entering a phase of consolidation.

Stage-wise: Late-stage funding explodes

The stage-wise breakdown tells a similarly dramatic story. Late-stage funding skyrocketed to US$6 billion in H1 2026, up 200 per cent from US$2 billion in H2 2025 and 137 per cent from US$2.5 billion in H1 2025. Seed funding also held firm, rising 68 per cent half-on-half to US$328 million, while early-stage investment dipped slightly to US$1 billion, down 8 per cent from US$1.1 billion in H2 2025.

Also Read: How SEA startups can build for real scale

The number of funding rounds, however, continued to decline, from 153 in H1 2025 to 147 in H2 2025, and now 127 in H1 2026. This divergence between fewer deals and far higher capital deployed underscores a broader global trend: investors are concentrating firepower into fewer, higher-conviction bets.

Singapore’s stranglehold on regional capital

The geographic concentration of funding is striking. Singapore-based tech firms captured 94 per cent of all funding in H1 2026, pulling in US$6.9 billion. Bangkok was a distant second at US$116 million (2 per cent), followed by Kuala Lumpur at US$104 million (1 per cent), Taguig at US$60 million (1 per cent), and Jakarta at US$38.2 million (1 per cent).

Singapore’s dominance is consistent with its all-time record of US$63.8 billion in cumulative tech funding, dwarfing Jakarta’s second-place $19.0 billion. For founders and investors across the rest of SEA, the concentration raises real questions about whether regional capital is becoming more geographically lopsided over time.

Exits: Quality over quantity

On the exit front, the story is nuanced. SEA Tech recorded 6 IPOs in H1 2026, down from 9 in H2 2025, but the quality improved markedly. AI company MiniMax debuted at a US$6.5 billion market capitalisation, while SkyeChip and JustCo also went public. The average IPO market cap surged to US$1.1 billion, up from US$270 million in H1 2025, a fourfold improvement.

Acquisitions totalled 19, down 44 per cent from 34 in H1 2025. The standout deal was the acquisition of ST Telemedia Global Data Centres by KKR and Singtel for US$5.2 billion, the largest acquisition in SEA tech in H1 2026, followed by Interplex, acquired by BizLink for $900 million.

Investors: Who’s writing the cheques

On the investor front, EDBI, Asia Partners, and Lion X Ventures led late-stage activity, whilst Vertex Ventures, SEEDS Capital, and Gobi Partners dominated early-stage deployment. At the seed level, Iterative and Antler each made four investments, with 500 Global placing three bets.

Among PE investors, Hillhouse topped the table with two investments, backing DayOne and Airwallex, while Coatue also backed both DayOne and Supabase.

One new unicorn, but the pipeline looks healthy

H1 2026 produced just one new unicorn; cashback platform ShopBack, which hit the milestone in February 2026 after 8.2 years from its Series A and US$350 million in prior funding across nine rounds. That compares to three new unicorns in H1 2025.

Also Read: Singapore’s dConstruct lands US$125M Series A to scale robotics for GPS-denied environments

However, the Soonicorn pipeline (companies likely to cross the US$1 billion valuation mark in the near term) added three new entrants: Startale (Tracxn Score: 63.7), EPG (49.4), and Edena Capital (29.7), all Singapore-based.

The bigger picture

Benchmarked globally, SEA accounted for 2 per cent of worldwide tech funding in H1 2026, ranking fourth by country behind the US (US$366 billion), China (US$21.1 billion), and the UK (US$14.5 billion), but on par with India (also at US$7.4 billion). With the region’s CAGR for funding over the last two years running at 56 per cent, the trajectory is clear: Southeast Asia is back in the game, and investors are paying attention.

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Data sourced from the Tracxn Geo Semi Annual Report, SEA Tech H1 2026. All figures are equity funding only and exclude debt, grants, post-IPO, and ICO funding.

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LinqAlpha, a New York-headquartered AI startup building intelligence tools for institutional investors, has raised US$22 million in a Series A round backed by a group of global and Asian investors.

The round was anchored by AVP, Atinum Investment, and GFT Ventures. It also drew participation from financial institutions and venture platforms across Singapore, Hong Kong, South Korea, Japan, and India, including SBI Investment, Z Venture Capital, Betatron Venture Group, East Ventures, SV Investment, Samsung Securities, Mirae Asset Venture Investment, Mirae Asset Capital, NH Investment & Securities, Shinhan Venture Investment, Hana Ventures, and NuVentures.

Also Read: From energy to ergonomics: 20 AI startups to watch in Southeast Asia

The company said the capital will be used to expand its global team, deepen integrations with market and alternative datasets, and accelerate deployment of its multi-agent platform across equities, macro, credit, and multi-asset strategies.

Founded by Jacob Choi, Subeen Pang, Jin Kim, and Hojun Choi, LinqAlpha brings together former Goldman Sachs analysts and MIT computer science PhDs. The startup says its platform is already used by more than 70 financial institutions across the US, Europe, and Asia, including sell-side sales, trading, and research teams at investment banks, as well as buy-side firms such as Causeway Capital Management and Schonfeld Strategic Advisors. Its buy-side clients collectively manage more than US$5 trillion in assets.

From search to synthesis

LinqAlpha is entering a market where institutional investors are drowning in data but still struggling to convert information into judgment quickly enough.

For public-market investors, the problem is no longer access. Earnings calls, regulatory filings, broker notes, central-bank commentary, shipping data, satellite imagery, social-media chatter, supply-chain signals, and credit-market movements are all available in some form. The harder task is linking these signals before they become obvious to everyone else.

That challenge is particularly acute in Asia, where market-moving events can cut across geographies within hours. A semiconductor supply-chain disruption in Taiwan or South Korea, a policy decision in China, a currency move in Japan, or a change in commodity demand from Southeast Asia can quickly influence equities, credit, macro trades, and sector views globally.

“The first wave of AI in finance made analysts faster. The next wave changes what they can know,” said Hojun Choi, co-founder and co-CEO of LinqAlpha. “The edge no longer comes from retrieving information; it comes from systems that surface market-moving signals before they are priced in.”

LinqAlpha’s pitch is that generic AI assistants are not enough for professional investors. Instead, the company allows institutional teams to deploy specialised AI agents that learn their investment frameworks, thesis history, research preferences, and feedback loops. These agents are designed to synthesise internal research with external market data, rather than simply retrieve documents or summarise information.

Jacob Choi, co-founder and co-CEO of LinqAlpha, described the product as “a second brain for every investment team”, built to turn accumulated research into actionable insight across liquid public markets.

Why Asian capital is paying attention

The investor syndicate is notable for its Asian depth. Beyond the lead investors, the round includes names from Japan, Korea, India, Hong Kong, and Southeast Asia. East Ventures, one of Southeast Asia’s most active early-stage investors, and Betatron Venture Group, a Hong Kong-based venture platform, are among the backers. SV Investment also brings links across Korea and Southeast Asia.

Also Read: In the age of AI, people matter more than ever

This matters because Asia is both a market opportunity and a stress test for products such as LinqAlpha. The region has complex cross-border capital flows, a fragmented data environment, multilingual markets, and a growing base of institutional investors that need to track global and regional signals simultaneously.

Singapore, in particular, has become a key hub for asset managers, hedge funds, family offices, and private banks. According to the Monetary Authority of Singapore, assets under management in the city-state stood at roughly US$4 trillion in 2023, making it one of Asia’s most important capital-allocation centres. The government has also been pushing the use of AI and data in financial services through regulatory sandboxes, AI governance frameworks, and industry collaborations.

Across Southeast Asia, the broader financial-services sector is also becoming more data-heavy. Digital banks, brokerages, wealth platforms, and institutional trading desks are generating and consuming more real-time information. While much of the region’s fintech investment over the past decade went into payments, lending, and consumer finance, AI infrastructure for financial institutions is now becoming a more serious category.

That shift is being driven by two forces: pressure on financial firms to improve productivity, and the need for differentiated insight in markets where information is increasingly commoditised.

A crowded race in financial AI

LinqAlpha is not alone in chasing this opportunity. The financial information market is already dominated by incumbents such as Bloomberg, LSEG, FactSet, S&P Global, and Moody’s, all of which are embedding generative AI into terminals, data products, and research workflows.

Bloomberg has developed finance-specific AI models and has been adding AI-powered search and summarisation to its ecosystem. FactSet and LSEG are also integrating large language models into analytics and workstation products. S&P Global has Kensho, which applies AI to financial and business data.

On the startup side, companies such as AlphaSense, Hebbia, Rogo, Dataminr, and RavenPack are competing across market intelligence, document search, alternative data, and financial research automation. AlphaSense, for example, has grown into a major player in AI-powered market intelligence by aggregating broker research, transcripts, filings, and expert-call content. Hebbia targets financial and legal professionals with AI agents for complex document analysis. Dataminr and RavenPack focus on real-time event detection and alternative data signals.

The competitive question for LinqAlpha is whether it can move beyond summarisation and search into persistent, investor-specific reasoning. Many AI tools can now condense a transcript or retrieve a filing. Fewer can adapt to how a portfolio manager or analyst builds conviction, tracks prior theses, and weighs contradictory market signals over time.

Manish Agarwal, General Partner at AVP, said LinqAlpha is targeting “a larger opportunity” than retrieval or automation: helping institutional investors discover differentiated insights in markets that reward speed, context, and proprietary judgment.

Execution will decide the outcome

For all the excitement around AI agents, the bar in institutional finance is high. Tools must be accurate, auditable, secure, and deeply integrated into existing workflows. Hallucinations, weak sourcing, or poor data lineage can be costly in investment decision-making. Financial institutions also tend to move slowly when adopting new systems, especially those touching proprietary research and trading processes.

LinqAlpha’s early traction with banks and buy-side firms gives it a stronger starting point than many AI startups selling into finance. But scaling across regions and asset classes will require more than model quality. The company will need robust data partnerships, enterprise-grade controls, and enough customisation to serve different investment styles without becoming a services-heavy business.

Also Read: AI is a game-changer, and here’s how your business can use it to win

The new funding gives LinqAlpha room to build that infrastructure. It also reflects a broader conviction among Asian investors that the next generation of financial technology will not simply digitise transactions, but reshape how capital-market professionals think, research, and act.

For Southeast Asia, where Singapore is increasingly competing as a global asset-management and AI hub, LinqAlpha’s round is another sign that institutional finance AI is becoming a serious investment theme. The winners will be those that can turn fragmented market noise into decision-ready intelligence before the rest of the market catches up.

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Treasurer, an AI financial technology company, used Echelon Singapore 2026 to introduce AlphaLenz, its enterprise AX solution for financial and insurance institutions, as it looks to deepen its footprint across Southeast Asia’s fast-growing fintech and enterprise AI market.

The company presented AlphaLenz at the two-day event, held from June 3 to 4 at the Suntec Singapore Convention & Exhibition Centre, as part of the Seoul Business Agency (SBA) Pavilion. The platform was shown to investors, fintech firms, financial institutions, insurers and enterprise partners, with Treasurer positioning it as a practical AI tool built for the realities of financial workflows rather than a broad, general-purpose assistant.

Built for financial data, not just general AI tasks

AlphaLenz draws on Treasurer’s core strengths in AI financial analytics, market data processing and financial intelligence automation. The solution began as a tool to support investment research and financial data analysis, but is now being expanded into an enterprise AX platform aimed at helping financial and insurance organisations automate repetitive tasks, speed up information processing and reduce the burden on teams handling large volumes of documentation.

Also Read: 5 ways generative AI is transforming the payments ecosystem

For banks, insurers and other financial enterprises, the challenge is rarely a shortage of data. The problem is making sense of it quickly. These firms handle market information, corporate disclosures, product and policy documents, customer-facing materials, internal reports, risk-related records and regulatory information, often across multiple systems and formats.

That means staff spend significant time on manual work such as collecting data, reviewing documents, summarising information, comparing records and preparing reports. Treasurer says AlphaLenz is designed to ease these processes.

Tackling repetitive workflows in finance and insurance

AlphaLenz combines AI agents, structured financial data workflows and industry-specific automation capabilities to support a range of enterprise use cases. These include financial data analysis, document review, market monitoring, report generation, product and policy information structuring, and internal knowledge search.

By automating repetitive analytical tasks, the platform is intended to free analysts, managers and back-office teams to focus on higher-value decision-making. That positioning is likely to resonate in a region where financial firms are under pressure to improve productivity while keeping headcount and operational costs in check.

Treasurer argues that this makes AlphaLenz different from off-the-shelf AI tools. Rather than being built for generic productivity, it has been designed around financial data structures and workflows specific to the sector.

Also Read: AI’s transformative role: Making insurance accessible and affordable globally

Its underlying technology is powered by Treasurer’s financial analytics engine, which processes Korean and Asian market data, company information, disclosures and industry trends. That foundation, the company says, allows AlphaLenz to deliver more relevant automation for enterprises operating in finance, insurance and capital markets.

Strong interest from Southeast Asia’s enterprise AI market

At Echelon Singapore 2026, Treasurer met with investors, fintech executives, financial institutions, insurance stakeholders and innovation teams. The company said it found strong interest in AI-driven workflow automation, cost-efficient financial data processing and enterprise AI adoption among organisations seeking to boost productivity with limited resources.

The reception highlights a broader trend across Southeast Asia, where financial institutions are increasingly exploring AI not just for customer service or front-end digital experiences, but for the less visible work that keeps operations moving: document handling, information retrieval, internal knowledge management and compliance-related processing.

For many firms, that back-office layer remains a major cost centre. Tools like AlphaLenz are being pitched as a way to streamline those functions without requiring organisations to rebuild their entire technology stack.

Treasurer eyes regional expansion

“Echelon Singapore 2026 was a meaningful opportunity for us to introduce AlphaLenz not only as an AI financial analytics platform, but as an enterprise AX solution for financial and insurance institutions,” said Ted Kim, CEO of Treasurer. “Financial companies are under increasing pressure to process more data, generate insights faster, and improve productivity with limited resources. AlphaLenz is designed to help these organisations automate repetitive workflows and make better use of their internal and external data.”

He added that Treasurer’s strength lies in its ability to structure complex financial information into actionable intelligence.

“Treasurer’s strength lies in AI-based financial analysis technology and the ability to structure complex financial data into actionable intelligence. By applying these capabilities to enterprise AX solutions, we aim to help financial and insurance companies improve work efficiency, reduce resource costs, and accelerate digital transformation across their organisations,” Kim said.

Treasurer joined the event through the SBA Pavilion, a programme aimed at helping promising Seoul-based startups expand into global markets. The company took part in exhibition showcases, demo presentations, private pitching sessions and AI-powered business matching activities, creating opportunities to connect with potential investors, partners and enterprise customers in Singapore and the wider region.

Next steps for AlphaLenz

Following the event, Treasurer plans to continue discussions with financial institutions, insurance companies, fintech firms and enterprise partners in Singapore and Southeast Asia. The company also intends to further develop AlphaLenz’s data coverage, AI agent capabilities and industry-specific workflows as it works towards broader adoption.

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

The move comes as enterprises across the region increasingly look for AI systems that can do more than generate text or answer simple queries. In finance and insurance especially, the real value lies in tools that can read, compare, organise and act on large and fragmented data sets with speed and consistency.

For Treasurer, AlphaLenz represents a bet that the next wave of enterprise AI in Southeast Asia will be less about flashy demos and more about practical automation. In that sense, the platform’s pitch is straightforward: less manual drudgery, more informed decision-making, and a smoother path towards digital transformation.

The post Treasurer unveils AlphaLenz at Echelon Singapore 2026 to target finance and insurance automation appeared first on e27.

As FitnessForce folds into US-based Daxko, the more interesting story is not the deal itself but what happens after it. Can a company built around the messy realities of India and the Gulf keep its product edge inside a larger US software group?

In this edited Q&A with e27, Hadi Curtay, former CEO of FitnessForce and now Managing Director at Daxko, argues that the company’s value was never about hype. It was built around localisation, operational detail, and software for fitness operators running across multiple sites and markets. He says India and the GCC remain the company’s strongest territories, while Southeast Asia is promising but uneven.

Also Read: The rise of ‘Strava Jockeys’: How Indonesia’s vanity economy is hacking the fitness tech ecosystem

Curtay also addresses a familiar founder anxiety after acquisitions: the team stays, the knowledge gets transferred, and the product slowly fades into somebody else’s roadmap.

The fundraising journey in emerging markets can be rough. Did regional VCs really understand the problem you were solving?

Not really, and that shaped how we built FitnessForce. Most investors understood the broad market story. It was not hard to see that fitness in India, the Middle East, and Southeast Asia was growing on the back of rising incomes, urbanisation, and greater health awareness. The harder part was explaining the kind of company we were building.

We were not another consumer app chasing millions of users. We were building operational software for fitness businesses with multiple locations, franchise layers, and a lot of moving parts. At the time, that did not fit neatly into the frameworks many investors were using.

The people who got it fastest were usually from adjacent industries such as hospitality, franchising, or multi-site operations. They understood how difficult it is to run one location well, let alone hundreds across countries. In the end, we spent more energy building with customers than chasing capital, and that discipline probably helped us.

Founders often worry about the acqui-hire trap. How did you avoid becoming just another team absorbed into a larger platform?

I think that concern is completely fair. I have seen it happen. A founder joins, the knowledge gets absorbed, the roadmap quietly loses momentum, and a year later the product is little more than a feature buried in a bigger system.

My co-founder, Quaid Jawadwala, and I were very clear from the beginning that we were not looking for a conventional exit. We wanted a partner that would let us keep building and scale the business further, not one that simply wanted our team or customer list. That ruled out a lot of conversations quite quickly.

What stood out with Daxko was that it did not approach the business as something to flatten into one generic stack. It runs purpose-built platforms and puts real operators in charge of them. My role is an operating role, not a ceremonial title. Quaid is also deeply involved in expansion, engineering, and product. We did not join to hand over the keys. We joined to keep building.

India is a fragmented market. Where are you genuinely strongest, and how big can that opportunity become?

We are strongest in the mid-market and premium end of the fitness industry in India. That ranges from ambitious single-site operators to large franchise groups and multi-location brands. Those businesses do not want software built for some entirely different market and then awkwardly forced on to them.

What matters in India is not just feature depth but operational fit. Payments, taxation, compliance, franchise structures, and customer behaviour all have local specifics. Our product has done well because it handles those realities while still supporting global operating standards. That matters more as international brands enter India and need systems that respect how they already run elsewhere but can still adapt locally.

Also Read: Healthtech in South and Southeast Asia – Seeing beyond the “obvious”

We have also invested heavily in areas such as multi-location management, central reporting, automation, franchise operations, and governance. Those capabilities become more valuable as operators go from a handful of sites to dozens or more. I still think the market is early, especially as organised chains continue to expand.

Can you point to one market where getting local details right really made or broke a customer relationship?

Saudi Arabia is the clearest example for me. It taught us early that localisation is not a translation exercise. A lot of software companies thought adding Arabic and local payment support was enough. It was not.

Operators there needed ZATCA-compliant invoicing. They needed proper right-to-left workflows, not translated labels dropped into a product designed for left-to-right use. They also relied heavily on WhatsApp, not as a secondary tool but as a core channel for renewals, confirmations, receipts, and day-to-day member communication.

I remember one customer coming from a European platform where staff were effectively running key workflows outside the software. Renewals were tracked manually, confirmations were sent separately, and management had poor visibility because too much was happening in disconnected processes.

We approached that differently. We built WhatsApp into the membership journey, strengthened Arabic and right-to-left experiences, and backed it with local-language support teams. Adoption moved quickly because we were supporting how operators already worked rather than asking them to adapt to somebody else’s assumptions.

Which markets do you lead in today, and where are you still earlier in the journey?

India and the GCC are where we have the strongest footing today. In India, the product has been shaped by a very fragmented and operationally demanding market, which forced us to become practical and resilient. In the GCC, especially in markets such as the UAE, Saudi Arabia, Kuwait, and Bahrain, we earned traction by localising properly across language, tax, payments, communication habits, and support.

Australia and Southeast Asia are more mixed. We are making progress, but I would still describe them as earlier compared with India and the GCC. The demand is there. Operators are becoming more sophisticated, and franchise growth is creating a clearer need for enterprise-grade software. But those markets are not all moving at the same speed or in the same way.

The US is different again. It is a mature market with higher expectations and tougher competition, but Daxko gives us a much stronger position there than we would have had on our own.

What does the Southeast Asia go-to-market strategy actually look like? Direct sales, partners, or following global franchises into the region?

It is a mix, and the balance changes by country. One of the fastest paths into Southeast Asia is through global franchise brands already expanding across the region. Those operators want a platform that can move with them across borders without forcing a fresh implementation every time they enter a new market. That plays to our strengths.

At the same time, I do not think you can treat Southeast Asia as one market. The regulatory environment, payment rails, and fitness culture vary significantly from country to country. That is where a lot of expansion plans go wrong. People talk about the region as if it behaves like a single operating market, and it simply does not.

So yes, global franchise expansion is an important entry point, but local execution still matters. You need to respect what is different in each market rather than applying one template and hoping for the best.

FitnessForce built more than 1,100 APIs and leaned into a headless architecture. Was that strategic, or was it forced on you by customer complexity?

It was both, but the strategic view came first. Quaid had a strong conviction very early that the platform had to be API-first. That was not a pitch-deck decision. It came from the belief that serious operators would eventually need flexibility, integrations, custom member journeys, and the ability to connect systems across markets.

Also Read: The most-funded healthtech startups in Southeast Asia: A decade in review

Then reality reinforced the point. Once you support operators across India, the GCC, Southeast Asia, Australia, and beyond, you quickly run into different payment systems, compliance requirements, communication habits, and workflows. A closed platform would have meant saying no far too often.

So the API depth grew from both vision and necessity. A principle we kept was that the APIs we use internally should also be available to customers. Headless should not mean a few token integrations on the side. It should mean the product is genuinely extensible.

What is the honest version of what could still go wrong after the deal?

The honest answer is that the biggest risk is not hidden in a clause somewhere. It is a values mismatch. In most acquisitions, if the two sides think differently about customers, products, people, or decision-making, the legal protections only carry you so far.

That was the main thing I paid attention to. We spent a lot of time understanding how Daxko makes decisions, how it treats customers, and what kind of company it wants to be. The more time we spent together, the more aligned we seemed.

That does not mean there is no risk. Any integration has execution risk. Priorities can drift, communication can break down, and good intentions do not automatically produce good outcomes. I am just less worried about the classic founder fear of being absorbed and sidelined, because this did not feel like joining a company that wanted to erase what made us work in the first place.

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