At Echelon Philippines 2025, a panel featuring Teddy Peralta of Altara Ventures, Jose Dalino of PayMongo, and Gian Paulo dela Rama of Sprout Solutions — moderated by Maansi Vohra of Monk’s Hill Ventures — explored the growing relevance of embedded finance for startups.

The discussion centred on the idea that financial services are increasingly becoming a core component of non-financial businesses, with panellists suggesting that every startup will, in some form, become a fintech company. The group noted that embedded finance is best suited to more mature startups with a stable customer base, where integrating financial tools can meaningfully deepen customer value and drive sustainable growth.

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Datakrew has raised US$2.6 million in a pre-series A round led by Greenwillow Capital Management, with participation from Beenext, 500 Global, SEEDS (now SG Growth Capital), XA Network, AngelList, and other investors.

It is not a monster round, and that is precisely the point: the Singapore-based deeptech startup is going after a market where trust is earned slowly, pilots are painful, and a single bad call can turn “AI insights” into a liability.

Also Read: 5 ways Indian EV makers can achieve world-class manufacturing efficiency

Founded in 2019, Datakrew sells what the EV industry increasingly needs but rarely standardises: battery intelligence for fleets and operators — tools that turn raw telemetry into signals about battery health, degradation, safety risk, and performance. Its core product, OXRED MyFleet, sits in the messy middle between vehicle hardware and business outcomes: fewer breakdowns, fewer roadside incidents, tighter maintenance scheduling, and better decisions on when to rotate, refurbish, or retire packs.

The company claims it has “recorded and analysed more than 10,000 battery assets across seven countries” and holds “over 105 million kilometres” of proprietary EV telemetry.

Datakrew also says its US-patented OBD-II device, ITUS Max, captures over 120 parameters, while OXRED MyFleet produces over 70 secondary metrics to estimate a battery’s future state of health.

The new money will fund products including OXRED GuardianAI and OXRED InsurShield, plus hires across sales and battery machine learning as it pushes into Europe and the Americas.

What this funding means for Southeast Asia

Southeast Asia’s EV story is often told through passenger cars and consumer adoption. Datakrew’s pitch is more industrial: the region’s near-term value is in commercial fleets — delivery vans, ride-hailing vehicles, buses, and two- and three-wheelers — where utilisation is high, and downtime is expensive.

That creates a natural opening for predictive battery maintenance, even if the market is still early. The region is fragmented across vehicle types, standards, climates, and charging behaviours. Heat, humidity, stop-start driving, inconsistent charging infrastructure, and uneven maintenance practices all accelerate degradation or, at a minimum, make it harder to predict. In other words: Southeast Asia is a harsh classroom for battery models—and a lucrative one if you can make them work.

How big is the market?

Hard numbers for “predictive battery maintenance” in Southeast Asia are scarce because spending is split across software subscriptions, telematics contracts, OEM warranties, workshop services, and insurance. A practical way to size it is as a serviceable market tied to commercial EV fleets: even modest per-vehicle annual spending on monitoring and diagnostics becomes meaningful once fleets scale, because batteries are the dominant cost centre and failures ripple through operations.

Also Read: Electric vehicles at the crossroads: Trust vs innovation

As fleets expand across Indonesia, Thailand, Vietnam, Malaysia, the Philippines, and Singapore, the addressable spend on battery analytics and risk tools plausibly moves into the hundreds of millions of US dollars annually over the next few years, with upside as electrification shifts from pilots to full fleet refresh cycles.

What drives growth in Southeast Asia

• Fleet economics: Operators can tolerate many things; they cannot tolerate unpredictable downtime.
• Financing and leasing: Lenders and lessors want better visibility into residual value and pack health.
• Insurance pressure: Higher repair costs and battery-related incidents push insurers towards telemetry-backed pricing.
• Regulatory direction: Safety expectations are rising, even if rules differ widely across countries.
• Second-life and resale: Better health data makes batteries easier to re-trade, repurpose, or warrant.

The catch: Southeast Asia is also where analytics vendors can die by a thousand integrations. Data access is inconsistent, OEMs guard diagnostic channels, and fleets run mixed vehicle brands. Any platform promising cross-fleet battery truth needs to survive the real world of missing signals, messy retrofits, and workshops that do not want more dashboards.

Where predictive battery maintenance in SEA is headed

The market is likely to move through three phases:

• Visibility (now): Basic health scoring, alerts, and anomaly detection—useful, but often descriptive rather than predictive.
• Decision support (next): Maintenance scheduling, charging policy optimisation, and pack rotation recommendations that are directly tied to cost and uptime.
• Risk and finance plumbing (later): Battery passports, warranty arbitration support, and insurance-linked products where analytics becomes part of contracts, not just operations.

Datakrew’s roadmap hints at that direction, especially with products framed around guardrails and insurance rather than only fleet dashboards. If battery analytics becomes embedded into underwriting, leasing, and warranty workflows, vendors gain stickier revenue and clearer ROI. They also inherit sharper accountability: if the model misses a failure, someone pays.

The US and Europe: bigger markets, tougher rules, stronger incumbents

Datakrew says it will expand into Europe and the Americas. The opportunity is straightforward: more EVs, bigger fleets, higher labour costs, and stricter compliance expectations—conditions that make predictive maintenance economically attractive.

Also Read: Electrifying Southeast Asia: Unleashing the radical potential of electric vehicles

Europe is heading towards deeper battery traceability and standardisation, including initiatives often described as a “battery passport” direction of travel. That increases demand for structured data, consistent diagnostics, and auditable health metrics across a battery’s life.

The US is a fleet-first electrification story in many segments—delivery, municipal vehicles, logistics—where operational uptime and total cost of ownership dominate purchasing decisions.

But the competitive reality is harsher. In mature markets, Datakrew competes not only with startups but also with:

• OEM platforms that already sit on privileged data.
• Tier-1 suppliers and diagnostics giants that can bundle analytics with hardware.
• Fleet telematics incumbents expanding into EV-specific insights.

In the US and Europe, the prize is large—arguably multi-billion-dollar over time when you include adjacent spend across telematics, diagnostics, warranty analytics, and insurance—but the bar is higher: security reviews, procurement bureaucracy, and legal exposure around safety claims.

Who else is fighting for this territory

Globally, the competitive set spans EV battery analytics specialists, broader telematics players, and OEM-adjacent platforms. Notable names include:

• Battery analytics specialists (global): TWAICE, ACCURE Battery Intelligence, Volytica Diagnostics, Qnovo, Eatron
• Fleet telematics expanding into EV insights (global): Geotab, Samsara
• OEMs and cell makers (global, indirectly competing): OEM-native diagnostics stacks and battery makers offering embedded monitoring and lifecycle services

In Southeast Asia, the picture is thinner: many fleets still rely on OEM dashboards and general-purpose telematics, while local integrators stitch together monitoring on a per-fleet basis. That gap is the opening Datakrew is trying to exploit—but it is also why credibility matters more than branding. Battery health is not a “move fast” domain. It is a “be right, then scale” domain.

Also Read: Inside Thailand’s EV and battery push: Balancing growth with sustainability

US$2.6 million will not buy domination. What it can buy is time: to prove models under Southeast Asia’s messy operating conditions, to land reference fleets, and to walk into Europe and the US with evidence rather than ambition. In battery intelligence, the difference is everything.

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Malaysian SMEs are embracing AI at an impressive speed, but a new report by Xero suggests this enthusiasm masks a deeper uncertainty that could hinder long-term progress. The study, Building a Future-Ready Economy: Examining AI Readiness and Adoption Among Malaysia’s MSMEs, describes this divide between optimism and confidence as a widening “confidence gap.”

According to the report, an overwhelming 81 per cent of Malaysian SMEs surveyed have already adopted some form of AI. Many see the tech as an essential part of future business operations, with 77 per cent believing AI will be standard practice by 2030. Another 75 per cent say AI will be beneficial to their business.

For now, most Malaysian SMEs are prioritising practical, short-term gains. The top expected benefits of AI include increased efficiency (63 per cent), cost savings (52 per cent) and improved employee productivity (48 per cent). As one business owner quoted in the report put it, companies are drawn to AI tools that “solve today’s problems before tomorrow’s ambitions.” Only 47 per cent associate AI with driving innovation, while a mere 33 per cent see it as a means for competitive differentiation.

Many firms are starting small, experimenting with accessible tools such as general-purpose conversational AI (55 per cent) and creative generative AI tools (38 per cent). These early steps suggest that Malaysian SMEs are primarily utilising AI for routine tasks rather than integrating it deeply into their core operations.

Yet the report’s central finding is that this enthusiasm is not matched by strategic confidence.

Also Read: With US$6M in support, GenAI Fund aims to close the gap between AI innovation and corporate adoption

Although adoption is high, 82 per cent of respondents say they need more education before they can implement AI with certainty. Only 56 per cent say they are familiar with business-relevant use cases, and 61 per cent admit they are overwhelmed by the sheer number of AI solutions and tools on the market.

This hesitancy results in what the study refers to as low intentionality—SMEs recognise the need to use AI, but many are uncertain about how to utilise it effectively. One respondent admitted that while AI tools are helpful for daily tasks, “trusting the technology with bigger decisions still feels risky.”

That lack of trust is one of the most significant barriers highlighted in the report. Data privacy and security top the list of concerns at 59 per cent, followed by fears of over-dependency on AI (51 per cent). Nearly four in 10 worry about the accuracy or quality of AI outputs, while 38 per cent point to ethical or plagiarism-related issues.

This uncertainty is reflected in the sharply divided attitudes toward AI-led decision-making. SMEs are split three ways: 33 per cent trust AI to make critical business decisions, 33 per cent do not trust it, and the remaining third remain neutral. According to the report, such indecision limits the value Malaysian SMEs can ultimately extract from AI.

Governance is another stumbling block. Among SMEs that have already adopted AI, 30 per cent have no policies or guidelines in place to govern its use. Without structured rules, many businesses are reluctant to expand their reliance on the technology. As the report notes, trust and responsible use are still “underdeveloped pillars” in Malaysia’s AI landscape.

Also Read: Exit or be left behind: The harsh new reality for SEA startups

Interestingly, cost is no longer the main obstacle to adoption. Instead, SMEs emphasise knowledge and guidance as their top priorities. When asked what would help them adopt AI more confidently, 61 per cent cited training and education, followed by access to technology (52 per cent) and advisory or consulting support (50 per cent). Financial assistance ranked far lower, with just 37 per cent saying grants or subsidies would make a significant difference.

This shift highlights a broader concern that businesses do not simply need more tools; they require the expertise to deploy them effectively.

The majority of SMEs also want stronger oversight. Nearly 68 per cent believe authorities should play a more active role in regulating AI in business, signalling a desire for structured safeguards and clearer national standards.

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Image Credit: Nicholas Chester-Adams on Unsplash

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In a connected world, we assume information flows freely. In reality, most ecosystems are fragmented: companies, institutions, and even countries operate in silos.

And where there are silos, there are opportunities.

The power of the bridge

Sociologist Ronald Burt once coined the concept of “structural holes”— the invisible gaps that exist between disconnected groups, organisations, or communities.

In every network — whether social, industrial, or geopolitical — value doesn’t just come from what you know, but who doesn’t yet know each other. Those who bridge gaps between clusters become information brokers: people or platforms who can translate, connect, and facilitate exchanges that others can’t.

Think of LinkedIn for professionals, Stripe for online payments, or Airbnb for spare rooms.

Each of them identified a “structural hole” — a broken or missing connection between two sides of a market — and built a bridge that transformed inefficiency into opportunity.

This dynamic is not theory. It is the basis of some of the most valuable business models in the world. Behind every marketplace, every platform, and every cross border service sits the same insight: when two groups need each other but have no efficient way to meet, whoever creates that pathway becomes indispensable. This is why categories like B2B marketplaces, global talent platforms, and intergovernmental digital infrastructure are expanding rapidly. The world isn’t short of capability; it is short of connection.

In Southeast Asia, these gaps are everywhere:

• Between local SMEs and global partners
• Between talented workers and companies abroad
• Between foreign investors and on-the-ground operators

Whoever builds the bridge owns the flow of trust, information, and eventually — value.

Why it matters now

As globalisation slows and regionalisation accelerates, the new competition isn’t between countries, it’s between networks: Whoever can connect supply chains, talent pools, and markets fastest will dominate the next decade of trade.

But while technology connects us, trust still lags behind.

Digital rails can be built quickly, but human confidence moves slowly. That is why many high-potential collaborations still die in the early stage — not for lack of opportunity, but for lack of a trusted interpreter who can manage expectations, translate cultural nuance, or reduce perceived risk. In emerging markets, this trust gap is often wider than the technology gap.

Also Read: From uncertainty to action: Power of AI and digital shaping deal strategies in turbulent times

Many founders and policymakers still operate within their local comfort zones, unaware that just one connection across borders could unlock exponential value.

This is where network aggregators — companies, platforms, or consultants that specialise in connecting these isolated clusters — play a vital role. They aren’t middlemen; they are multipliers.

They compress time. They reduce friction. They turn what would otherwise be a six-month relationship-building exercise into a six-day warm introduction. In capital markets, they become credibility amplifiers; in talent markets, they become mobility engines; in supply chains, they become resilience builders.

The opportunity for builders

If you’re a founder or strategist, look for gaps, not crowds. Ask:

• Who in my industry doesn’t talk to each other — and why?
• What friction prevents partnerships from forming?
• How can I make the first connection easier, faster, or safer?

In fragmented markets, the one who connects others doesn’t just create value: they control it.

And as ecosystems become more interdependent, the advantage of the connector will only grow. The next iconic companies will not only build products — they will build bridges.

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I was sitting in the studio at Channel News Asia, recording a podcast on agentic AI. We were talking about tools. Workflows. The future of jobs. At one point, the hosts shared something simple: They had been prompting AI, and the output just wasn’t right.

So they adjusted the prompt. Then adjusted it again. And suddenly, the output improved.

That’s when it clicked for me. This wasn’t an AI problem. It was a communication problem.

AI is not the problem — your instructions are

Most founders think AI is about tools. Which tool to use? Which model is better? Which platform is more “agentic”?

But after building and training my own systems, I’ve realised something much simpler: AI doesn’t fail because it’s not smart enough. It fails because we’re not clear enough.

When AI gives you a generic output, it’s not a capability issue. It’s a clarity issue.

The uncomfortable truth: AI is exposing your thinking

AI responds in seconds. Which means your thinking gets reflected back to you… immediately.

If the output is off, vague, or misaligned? That’s not a delay. That’s the diagnosis.

AI doesn’t wait for you to realise you were unclear. It shows you instantly.

With humans, it’s different. They still execute. They:

• Fill in gaps.
• Make assumptions.
• Try to “figure it out”.

And sometimes, they even deliver something that looks correct.

But let’s be honest: Output ≠ understanding.

Also Read: Cruising the startup ocean: Sailing toward an unfixed horizon

The illusion most founders are operating in

Here’s the slightly uncomfortable part. A lot of teams are not aligned. They’re just… coping. Work gets done. Slides get delivered. Campaigns get launched.

But underneath:

• Expectations are misaligned.
• Thinking is inconsistent.
• Time is wasted fixing avoidable mistakes.

Why don’t people ask?

Because:

• They don’t want to sound like they don’t understand.
• They assume they’ll figure it out.
• Or they interpret based on their own logic.

AI doesn’t do that.

It either:

• understands
  or
• exposes that it doesn’t.

There’s no ego. No masking.

The framework: CLEAR briefing system

If prompting AI feels hard, it’s because briefing is hard. So here’s a simple model I use across both AI and teams: C.L.E.A.R.

• C – Context: What is happening? Why does this task exist? → “We’re launching a webinar to convert leads into a paid programme.”
• L – Logic: How should this be approached? What thinking model is used? → “Use a Hook → Story → Offer → CTA structure.”
• E – Expectation: What does success look like? → “Conversion-focused, not just informational.”
• A – Aesthetic / Angle: What is the tone, style, or positioning? → “Direct, structured, slightly provocative.”
• R – Result Format: What exactly should be delivered? → “Write a 60-second talking head script + captions for three platforms.”

Why does this work? Because most people skip at least two to three of these. They say, “Help me write a post.” And expect:

• Clarity
• Alignment
• Quality

That’s not prompting. That’s hoping.

Also Read: Turning sustainability into a growth strategy for Singapore SMEs

Mini “How-to” for founders (you can apply this today)

If you’re using AI – or managing a team – try this:

• Step 1: Take your last instruction. Something like: “Create content for my event.” Now rewrite it using CLEAR.
• Step 2: Compare the output. You’ll notice:

• Less back-and-forth.
• Higher quality output.
• Better alignment.

• Step 3: Watch your own thinking. This is the real game. If you struggle to:

• Define the outcome.
• Explain your logic.
• Articulate expectations.

That’s not an AI problem. That’s a thinking problem.

One thing AI taught me about myself

There are days when I get lazy. I give shorter instructions. Less context. I assume continuity.

And when the output comes back wrong, I catch myself thinking: “Why is this off?”

Then I realise:

• I didn’t reset the context.
• I didn’t clarify that it was a new task.
• I assumed understanding.

The AI didn’t misunderstand me. It followed exactly what I said. Just not what I meant.

Let’s make this a little uncomfortable

We like to say: “AI isn’t good enough yet.” But here’s the real question: Are you clear enough yet?

Because right now, the gap isn’t just:

• Human vs. AI.

It’s:

• Clear thinkers vs. unclear thinkers.

And the scary part? AI is amplifying both.

The future of work isn’t AI vs. humans

You’re not competing with AI. You’re competing with people who:

• Can think clearly.
• Communicate precisely.
• And leverage AI effectively.

And those people? They move faster. They execute better. They scale without friction.

AI is not replacing leadership. It is exposing it.

And in the AI era, clarity is no longer optional. It’s your competitive advantage.

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