Southeast Asia’s startup ecosystem continues to mature rapidly, with founders increasingly building companies that operate across multiple markets from day one. Expansion into hubs such as Singapore, Hong Kong, the UAE, and the United Kingdom is often a natural next step, but navigating incorporation requirements, compliance obligations, accounting standards, and corporate governance can quickly pull focus away from innovation and growth.

As digital business infrastructure evolves globally, companies are looking for streamlined, technology-enabled solutions that reduce administrative overhead while maintaining regulatory confidence. This need is particularly pronounced in Southeast Asia, where regulatory environments vary widely. Osome addresses these challenges by combining AI-enabled software with expert human support, helping businesses manage incorporation, accounting, compliance, and corporate secretarial requirements so founders can focus on building and growing their businesses instead of financial admin.

Meeting Osome at Echelon Singapore 2026 offers founders and operators a chance to explore how streamlined business infrastructure can support faster expansion. Whether entering Singapore for the first time, scaling regionally, or preparing for international growth, practical guidance on compliance and operational setup can make a measurable difference in execution speed and confidence.

Osome: Leading the way in digital business services

Osome is a digital business service provider focused on helping entrepreneurs start, manage, and grow companies across Singapore, Hong Kong, the United Kingdom, and selected international markets. Its mission centres on making entrepreneurship more accessible by removing administrative and compliance barriers that often slow early stage and growth stage companies.

The company combines AI and automation with professional expertise to deliver incorporation support, accounting services, corporate secretarial solutions, and compliance management through an integrated digital platform. This hybrid approach allows businesses to benefit from efficiency and transparency while still having access to experienced professionals when navigating regulatory requirements.

Osome’s regional relevance is particularly strong in Southeast Asia, where cross-border expansion is increasingly common. Partnerships with venture capital firms, accelerators, incubators, co-working spaces, and other ecosystem enablers further strengthen its role as a practical support layer for founders scaling across markets.

Also read: Scaling Southeast Asia: Who to meet at Echelon Singapore 2026

Meet Osome at Echelon Singapore 2026

Osome joins Echelon Singapore 2026 as a preferred incorporation partner, alongside other ecosystem leaders, founders, investors, and innovators gathering at Suntec Singapore CEC on 3-4 June 2026. The two-day event brings together the region’s startup community through content stages, exhibitions, networking sessions, and knowledge-sharing opportunities designed to support Southeast Asia’s innovation economy.

Attendees can connect with the Osome team to discuss incorporation strategies, compliance planning, and cross-border expansion considerations. The company will offer consultation packages and discounted incorporation services tailored for startups and SMEs attending Echelon, creating a practical entry point for businesses preparing to scale regionally or internationally. Book a call with Osome here to get started.

As Southeast Asia’s innovation economy continues to evolve, strong operational foundations are becoming essential to sustainable growth. Collaboration between founders, infrastructure providers, investors, and ecosystem enablers helps ensure that companies can scale efficiently while remaining compliant and resilient. Platforms like Echelon play a key role in enabling these connections and supporting the next phase of regional innovation.

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

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When global researchers predicted that speech recognition for low-resource languages wouldn’t be commercially viable until 2030, we had already achieved 97 per cent accuracy in 2020. This isn’t a story about one company – it’s about how developing nations can build AI sovereignty by solving real problems, building infrastructure patiently, and getting creative with talent.

After 25 years of building language technology in Mongolia – from basic character rendering to complex reasoning systems – I’ve learned that the path to AI independence requires three things: sustainable development, pragmatic focus on real missions, and a bigger vision. Here’s what actually works.

Start where problems challenge society

Silicon Valley starts with solutions looking for problems. Developing nations must start with problems demanding solutions. Mongolia’s AI journey began with frustrations that might sound trivial to outsiders but were paralysing daily life:

• The typing crisis: We literally couldn’t type in our traditional vertical script. Eight centuries of written culture were becoming digitally extinct. This wasn’t a market opportunity – it was cultural death in slow motion.
• The dictionary that wasn’t: No comprehensive digital Mongolian-English dictionary existed. Students and professionals relied on dusty paper editions from the 1960s. Every missing translation represented a lost opportunity.
• The spellcheck absence: Mongolian text had no spell-checking. Government documents, business contracts, and academic papers were riddled with errors that undermined credibility and caused actual legal disputes. That was one of the biggest social problem until 2017.
• The language corruption crisis: Here’s something that keeps me awake at night – we’re raising a generation that speaks broken Mongolian. Young people unconsciously rely on Google Translate for homework, social media, and daily communication. But Google Translate’s Mongolian support is so poor that it’s literally corrupting our language. Students write essays in grammatically incorrect Mongolian, thinking it’s proper because “Google said so.”
• The speech recognition gap: Parliament and government operate through meetings, making transcription a critical function. Without speech recognition, protocol-keeping consumed enormous human resources. And solutions had to be on-premise for security.
• The service crisis: Banks and telecoms haemorrhaged money on customer service. Without automated Mongolian language support, every query required human agents.
• The procurement corruption: At Erdenet Mining, Mongolia’s largest company, manual tender evaluation enabled systematic corruption. Millions disappeared into rigged contracts or excessive delays in purchase completion.

Each problem we solved provided data, experience, and credibility for the next challenge. Our fully automatic tender evaluation system at Erdenet didn’t just save money and reduce corruption – it generated the revenue and trust that funded our speech recognition and language model research.

This problem-first approach built our capabilities organically. By the time we tackled large language models, we had accumulated 20 years of linguistic data, domain expertise, and customers who trusted us.

Harness your diaspora’s expertise

Mongolia has more software engineers in Silicon Valley, Berlin, Seoul, and Tokyo than in Ulaanbaatar. This brain drain, common across developing nations, became the cornerstone of our talent strategy.

The diaspora advantage

We couldn’t compete with FAANG salaries. Instead, we offered something money can’t buy: the chance to build their homeland’s technological future. Our pitch was simple: “Your skills + our mission = your legacy.”

AI Engineers from Germany, Switzerland, Italy, and the US were invited to work on-site, fostering knowledge exchange with local talent. They mentored, contributed code, and validated technical decisions, creating a sustainable talent flow instead of a one-way brain drain. This approach assured young engineers that they could venture out, gain experience, and return to impactful work.

Also Read: On-chain data and Web3 security: Insights from industry experts

Build infrastructure with extreme patience

Infrastructure has been our greatest challenge from day one. We move slowly, but we move forward. Our datacenter evolution tells the story of patient building:

We started in 2017, purchasing two gaming GPUs (RTX 2090 Ti). By 2019, we had received angel investment to buy our own L40S GPUs. In 2021, we added A6000 units. Today, we run our own infrastructure with local hardware for sensitive data. We use cloud resources from AWS and Google to train bigger models.

We manage everything ourselves – not by choice, but by necessity. No managed services exist for our use cases. This forced us to develop deep infrastructure expertise that later became a competitive advantage.

The lesson? Start with whatever computer you can access. Our first speech recognition model was trained on two gaming GPUs for one year. Perfect infrastructure is a luxury, developing nations can’t afford to wait for.

Competitive advantage

Cultural localisation

The assumption that AI models can simply be “translated” fundamentally misunderstands how language and culture intertwine.

The mixed language reality

Mongolians don’t speak “pure” Mongolian. Never have, never will. Real conversations flow like this:

• Mongolian grammar structures with English tech terms embedded
• Russian expressions from the Soviet era sprinkled throughout
• Chinese trade phrases when discussing business

Our models had to understand “Kodoo push hiigeed product ownertoo мэдэгдчихлээ.” (I’ve pushed the code and notified the product owner.). When we insisted on linguistic purity, our models failed spectacularly. When we embraced messy reality, they worked.

Release strategy that makes sense

While big tech companies focus on the “top 20 languages” and throw everything else into an “other” bucket, we take a different approach. For each market, we focus on the languages that actually interact. For Mongolia, that means:

• Mongolian (obviously)
• English (global connectivity)
• Russian (historical ties)
• Chinese (trade relationships)

This connected-language approach delivers 10x better results than generic multilingual models.

Also Read: High adoption, low understanding: The Philippines’s blockchain knowledge gap

Data sovereignty

Your data will be securely stored within your country’s territory, allowing you to use it with peace of mind. If governments fail to actively embrace and utilise artificial intelligence, they risk falling behind, potentially leading to significant social inequality within their societies.

Digital sovereignty through pragmatic building

True sovereignty means controlling critical layers while pragmatically using what works.

What we built ourselves:

• Character encoding and input methods (100 per cent local)
• Finate state automata and transducers as language complexity (100 per cent local)
• Speech recognition and synthesis (100 per cent local)
• Core language models (100 per cent local)
• Application frameworks (80 per cent local, 20 per cent open source)
• Infrastructure management (60 per cent local, 40 per cent standard tools)

The honest trade-offs:

• We use NVIDIA hardware (no alternative yet)
• We leverage open-source frameworks (why reinvent PyTorch?)
• We adapt international research (standing on the shoulders of giants)
• But we control all critical paths and data

Economic sustainability:

Sovereignty without economic sustainability is just expensive nationalism. Our model evolved through necessity:

• Government as first customer, not sugar daddy
• Enterprise solutions funding research
• Open source contributions building global goodwill

The uncomfortable truths

Let me be honest about what building AI in a developing nation really means:

• You’ll never have enough resources. We have 1/1000th of OpenAI’s budget. Make constraints drive innovation. Our GPU shortage forced us to optimise models that now run on minimal hardware
• Technical debt is inevitable. Plan refactoring cycles from day one. We still maintain code from 2017 because it serves critical government systems.
• International competition will arrive. Build cultural moats early. By the time OpenAI or Google supports Mongolian properly, we’ll have ten years of local context they can’t replicate.
• Talent will always be scarce. Create compelling missions. We can’t match Silicon Valley salaries, but we offer something better: the chance to preserve their culture through technology.
• Trust is your biggest challenge. When we first claimed 97 per cent accuracy in Mongolian speech recognition, nobody believed us. International researchers said it was impossible. Now, the customers and government say, “Why not just use ChatGPT?” You need rock-solid local success stories. Building credibility took years of consistent delivery.

Also Read: Asia rises in the AI chip race: China to outgrow US by 30 per cent by 2030

The future is distributed

The era where a few companies in a few countries control global AI is ending. The future belongs to distributed, culturally-rooted AI systems serving specific populations with a deep understanding.

Mongolia’s journey from missing keymaps to billion-parameter models proves that developing nations don’t need charity. Don’t blame Silicon Valley for ignoring your market – they don’t owe you anything. Build it yourself.

With sustainable development focused on real problems, pragmatic building strategies, and a vision for digital sovereignty, any nation can achieve AI independence. Don’t be discouraged if someone says you can’t compete with OpenAI or Anthropic. While they are currently reducing their model parameters, we are increasing ours. In the near future, we will converge in the middle. The first to reach that point could be the winner, but nevertheless, business applications using models under 30B parameters will capture 90 per cent of the whole AI market share.

The question isn’t whether to build homegrown AI, but whether your problems are painful enough to sustain the decades-long journey to solve them. For us, watching our youth lose their mother tongue to bad machine translation was painful enough.

But when a nomadic herder can speak to AI in their own language and get help accessing government services? When corruption drops because algorithms can’t be bribed? When does your culture live digitally for future generations? Egune AI is making it possible.

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Over the past two quarters, I’ve been teaching digital transformation in the era of 5G and AI, which is something new to me, but deeply rewarding. One of my first questions to every new class is simple: “What brought you here?”

Surprisingly, more than half the class often says they’re here because their SkillsFuture credits are expiring soon. They’re not chasing certificates or career pivots — they simply don’t want to waste the opportunity to learn something relevant in a fast-changing world.

Many of them are staff from SMEs, people who didn’t come in with big expectations but with an open mind. By the end of the course, they often walk away feeling genuinely proud — not just for learning new skills, but for discovering that they can.

It made me reflect on something remarkable about Singapore’s ecosystem. While we often talk about being a “Smart Nation” in terms of infrastructure and innovation, what’s truly smart is how the government has quietly built a learning mindset into the fabric of society. Every citizen has access to resources to up-skill, re-skill, or simply stay curious, and that creates a compounding effect across the workforce.

The ripple effect on startups and SMEs

When we think of innovation, the spotlight naturally falls on the usual suspects — the Y Combinators, the Antlers, the tech founders experimenting with Claude 4.5 for code generation, Meta’s AI glasses, or OpenAI’s agents. These are the pioneers pushing the frontier.

But real transformation happens when AI and digital literacy reach the everyday office worker — the operations executive, the logistics coordinator, the sales manager. The future isn’t just being built in accelerators or venture labs; it’s being sustained by people in SMEs who are now using digital tools, automating manual processes, and thinking differently about their work.

Technology only fulfils its potential when it becomes widely usable. No matter how advanced a model is, if only a few understand it, it remains an elite game. But when the broader population starts to experiment — even in small ways — that’s when innovation scales.

Also Read: Building the future: Up-skilling and empowerment in India’s real estate boom

Giving the “back row” a front-row seat

In every learning journey, there’s a simple truth: we don’t know what we don’t know. AI is evolving so fast that even tech professionals struggle to keep up. For non-tech professionals, programs like SkillsFuture create an entry point — giving them a shared vocabulary, a way to join the conversation instead of being left behind by it.

Owning an AI tool today is like owning a smartphone 15 years ago. You might not use every feature, but it changes how you live, work, and connect. The same applies to AI – it’s becoming the new baseline for digital fluency.

The dual movement of a smart nation

What I find admirable about Singapore’s approach is its two-speed innovation model: one that accelerates frontier technologies through startups and MNCs, and another that ensures the entire society moves along with it. The front-runners may be building the future, but the rest are being equipped to live and thrive in it.

That’s what makes the ecosystem resilient. Startups can scale faster when the broader workforce is digitally competent. SMEs can adopt innovation without fear. And individuals, regardless of age or industry, can adapt rather than resist change.

A shared momentum

Whether you’re at the front driving innovation, at the back learning to catch up, or somewhere in between enabling the system, we’re all part of the same movement.

The next wave of transformation won’t just be about smarter technologies; it’ll be about smarter people using them with confidence. And if there’s one thing Singapore’s story shows us, it’s that when everyone learns, the nation moves forward together.

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Today’s workforce is undergoing rapid transformation. As AI reshapes every industry, organisations and employees are discovering new ways of working, creating a need for different professional skills and knowledge. For employees, this has both opened opportunities and sparked anxiety.

Many are experiencing FOBO (Fear of Being Obsolete), with nearly half of professionals worrying their skills will be outdated within five years. At the same time, 94 per cent say they would stay longer at an organisation that invests in career development, showing that up-skilling opportunities are a top priority for professionals.

Yet, despite the necessity and demand for learning, skills gaps remain from digital fluency to problem-solving and adaptability. This work landscape is proving to be particularly challenging for Gen Z, who are newer to the workforce and must learn foundational professional skills at a time of rapid change. Older employees may also find this integration of new technology overwhelming and need support with digital skills.

The challenge for organisations is clear: provide employees with the learning support they need to prepare for the future of work. Edutech offers a pathway to achieving this goal by delivering scalable, engaging, and personalised learning opportunities tailored for modern employees.

Drive continuous up-skilling and adaptability with micro-learning

With AI accelerating the pace of change, organisations can no longer afford to only deliver employee training in all-day sessions once or twice per year. Instead, edutech tools enable managers to embed learning into the flow of everyday work with micro-learning. Mobile-friendly and bite-sized training formats ensure learning can happen anytime, anywhere, and are especially appealing to Gen Z as digital natives. These sessions are easier to fit into employees’ busy schedules without disrupting work routines, while also making learning a continuous process that can be quickly adapted according to changing needs.

Micro-learning can be designed to help employees build skills and knowledge step-by-step as they need it. This lets employees see the relevance of the training right away, which is a key factor in keeping learners engaged and helps employees avoid information overload.

Also Read: The future of edutech: Personalising learning for all

Edutech can also make it easier for employees to track their learning progress, filling knowledge gaps and developing new skills at their own pace. For employers, integrated reporting helps track participation across training sessions, identify skill gaps, and refine future learning strategies. With full visibility of their workforce’s strengths and weaknesses, and how they respond to the training in real time, trainers can continuously adapt their approach to be more efficient and impactful.

Add interactivity to make learning engaging and effective

Traditional training methods like long modules, static slide decks, and one-way seminars often fail to keep employees motivated. With edutech, trainers can design learning experiences that are interactive, using gamification, real-time feedback, and adaptive learning paths that make sessions memorable and effective.

In fact, research has shown that interactive learning lowers employee attrition rate by 37 per cent.

A new innovative approach to training doesn’t need to take a lot of extra time or effort, either. Organisations can use AI to transform existing training materials into interactive or even gamified experiences. AI-enhanced edutech can also make it easier to personalise learning, giving employees highly relevant training that’s suited to their needs and learning styles. With the right tools, even compliance training or technical refreshers can become experiences employees look forward to.

Encouraging collaboration and knowledge sharing

Beyond individual learning, an AI-ready workforce requires collaboration and collective problem-solving. Peer-to-peer learning and knowledge exchange sessions can be a powerful way to connect teams while promoting learning. However, many employees would hesitate to give a presentation to their colleagues even with valuable information, simply for fear of boring their coworkers.

Edutech platforms can help professionals turn these dry sessions into exciting and energising activities by using friendly competition or collaborative challenges. Session hosts can test their coworkers’ knowledge with gamified quiz questions, and use live polls, Q&As, and brainstorming to ensure everyone has a voice, even in hybrid or global teams. Employees can also use AI tools to create these peer learning sessions, helping them build AI skills at the same time.

Also Read: Edutech in SEA is ripe for acceleration. This is why they can help build a more inclusive society

Preparing today for tomorrow’s workforce

As AI transforms industries at a lightning-fast pace, the workforce must evolve just as quickly. Edutech can play a crucial role in this transformation by making learning engaging, accessible, and collaborative, and enabling organisations to deliver it at scale across their workforces.

Organisations that invest now in embedding edutech into their talent strategies will not only be better equipped to future-proof their workforce; they can also unlock new levels of productivity, retention, and innovation. Ultimately, the intersection of AI and edutech is about empowering people with the confidence, curiosity, and collaboration skills they need to thrive in the years ahead.

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In the creator and startup world, “community” has become a buzzword.

Everyone wants one. Creators launch Telegram groups. Founders open Slack channels. Professionals start networking circles.

But most communities don’t monetise. And it’s not because of low engagement. It’s because there is no economy running behind them.

At our recent “Community That Converts” session, this pattern became obvious. The room was filled with creators, early-stage founders, and corporate professionals trying to pivot. They didn’t lack ambition. They didn’t lack skill. Many of them already had audiences.

What they lacked was structure.

A group is not an ecosystem

There’s a misconception that if you gather enough people in one place, value will naturally circulate.

It doesn’t. A group is a container. An ecosystem is infrastructure.

An ecosystem has:

• Defined roles.
• Defined value propositions.
• Defined price points.
• Defined outcomes.
• Defined pathways for exchange.

Without these, what you have is a connection. Not commerce.

And connection alone is not sustainable.

Even charities need funding to operate. Even volunteer-driven movements require resources. In today’s economy, value is transacted through money. If no monetary exchange exists, sustainability becomes fragile.

The hard truth is this: If your community has no internal economy, it will eventually stall.

The “more content first” trap

One recurring belief we encountered was this: “I need more content first.” More posts. More reach. More followers. But content amplifies what already exists.

If the value proposition is unclear, more content amplifies confusion. If the offer is undefined, more visibility magnifies the gap. People cannot pay for what they do not understand.

And many community builders avoid pricing not because they are unsure of their audience, but because they are unsure of their own value.

Also Read: Customer churn analysis: How can startups get it right?

Monetisation is not hard selling — it is a measurement

Monetisation is often framed emotionally, as something uncomfortable or aggressive. But monetisation is simply an exchange of value transacted through money. Money measures clarity.

If no one is willing to pay, one of three things is usually happening:

• The outcome is vague.
• The positioning is weak.
• The offer is undefined.

You cannot expect payment without a price tag. You cannot expect sustainability without structure.

Consider this in professional terms. You do not pay an intern a manager’s salary. You do not pay a junior consultant a partner’s rate. Price is determined by positioning, experience, outcome, and brand equity.

The same applies to communities.

If members do not know:

• What they are offering,
• What problem do they solve?
• What outcome do they deliver?
• What they charge,

There can be no internal economy. And without economy, there is no conversion.

From individual hustle to layered ecosystems

A monetisable community is not about everyone paying one central figure. It is about enabling structured value exchange within the ecosystem.

Imagine a business-focused environment that includes:

• A strategist.
• A videographer.
• A photographer.
• A PR consultant.
• A lead generation specialist.
• A copywriter.

Each has a defined value proposition. Each has pricing. Each understands positioning.

When someone in the ecosystem needs support, they do not go outside. They transact within. That is when a community becomes infrastructure. That is when it converts. The platform itself becomes the enabler of value exchange.

Belonging is necessary, but directional

Belonging matters. But belonging without direction becomes noise.

Communities thrive when members share a similar outcome. An investment-focused group works because members aim for capital growth. A health-driven group works because members prioritise wellbeing. A business-focused ecosystem works because members want leverage, revenue, and growth.

The shared ambition defines the economy. Not just shared interests, shared direction.

Also Read: From bridge rounds to global awards: Startups across Asia keep building

Why most communities fail

Most communities fail because they are built emotionally, not structurally.

They prioritise:

• Inclusiveness without tiers
• Engagement without offers
• Networking without pricing
• Connection without defined outcomes

It feels good initially. But without an economic layer, energy dissipates. The most sustainable communities are both inclusive and exclusive. Inclusive in knowledge-sharing and access. Exclusive in-depth, commitment, and paid tiers.

Open the front door. Structured inner rooms. Free distribution. Paid progression.

That structure is what enables revenue, confidence, clarity, and leverage — not just for the founder, but for the members themselves.

The blueprint: What makes a community convert

A converting community requires five foundational elements:

• Clear value proposition: Members must know exactly what they offer and to whom.
• Defined outcomes: The community must rally around a specific ambition or transformation.
• Structured pricing: Offers must be priced clearly. No ambiguity. No hidden discomfort.
• Tiered access: Free access for exposure. Paid tiers for depth and accountability.
• Facilitated exchange: The platform actively enables members to transact within the ecosystem.

When these layers exist, monetisation is not forced. It becomes natural. Because value is visible.

The bigger shift

The creator economy is maturing. Visibility is abundant. Content is easy. AI compresses execution.

What differentiates sustainable founders now is not output. It is ownership. Ownership of:

• Audience channels.
• Pricing structures.
• Ecosystem design.
• Internal economies.

A large audience without structure is fragile. A smaller, well-architected ecosystem is durable.

A community that converts is not about building a room full of people. It is about building a marketplace of defined value.

And once value can be exchanged clearly, sustainably, and repeatedly, conversion is no longer accidental. It is engineered.

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