South Korea is experiencing a transformative AI boom, positioning itself as a global leader in artificial intelligence innovation. Driven by its world-class expertise in semiconductor technology—essential for AI systems—South Korea has become a pivotal hub for AI-related advancements.

The country’s dominance in memory chips, particularly those used in AI data storage and processing, has attracted significant international attention. Global companies such as NVIDIA are relying on South Korean firms like SK Hynix and Samsung for cutting-edge high-bandwidth memory (HBM) solutions.

The South Korean government is heavily investing in AI and semiconductor infrastructure, exemplified by initiatives like the US$471.4 billion Yongin Semiconductor Cluster. This has led to a surge in investor confidence and boosted the global profile of local startups. Companies such as Naver, LG, and Kakao are at the forefront of AI development, from generative AI chatbots to advanced cloud infrastructure.

Moreover, collaborations with global tech leaders, including OpenAI, Google Cloud, and Amazon Web Services, have further accelerated South Korea’s AI capabilities. These partnerships underscore the country’s growing importance as a strategic player in the AI landscape.

Also Read: From innovation to impact: Key sectors driving GenAI adoption in ASEAN

South Korea’s vibrant AI ecosystem, bolstered by government support and robust international collaborations, has made it a hotspot for innovation. It nurtures startups that are shaping the future of artificial intelligence globally.

Below is the list of South Korea’s top AI startups:

Rebellions

Rebellions manufactures AI accelerators. It develops AI processors using silicon-based architecture and deep learning algorithms and offers custom processors with optimised algorithms. These processors have applications in financial trading, energy systems, cloud servers, and autonomous vehicles.

Founding year: 2020
Total funding raised: US$211 million
Investors: Aramco, Wa’ed Ventures, KT, Korean Development Bank, Korelya Capital, KT Cloud, Pavilion Capital Partners, Shinhan Venture Investment, DG Daiwa Ventures, Somers Investment Partners, Korea Development Bank Europe, Mirae Asset, IMM Investment, KB Investment, Kakao Ventures, Mirae Asset Capital Markets, STH, GU Equity Partners, KT Investment, Shinhan Capital, Dunamu & Partners, and ReVentures.

DeepX

DeepX designs and manufactures neural network processing units for edge applications. The startup also develops an AI-powered bot that keeps crypto socials free from scammers and spammers.

Founding year: 2018
Total funding raised: US$103 million
Investors: SkyLake, BNW Investment, Time Polio Asset Management, Aju IB Investment, Korea Development Bank Europe, ALUMNI-PARTNERS, Pathfinder H, Shinhan Bank, Capstone Partners, Shinhan Financial Group, DS, InterValue Partners, Magna, and Kingo.

Selectstar

Selectstar provides a platform for AI training solutions. It allows users to collect images, voices, videos, and texts from different sources and label and validate the raw data. Selectstar’s features are object segmentation, line segmentation, classification, transcription, editing, comparison, and other tools for labelling solutions.

Founding year: 2019
Total funding raised: US$67 million
Investors: Kakao Ventures, Company K Partners, CJ Investment, Now IB Capital, and Kolon Investments.

Wrtn Technologies

Wrtn develops an AI and NLP-based writing assistant to enhance creativity and productivity.

Founding year: 2021
Total funding raised: US$32 million
Investors: SkyLake, BRV Capital Management, Z Venture Capital, Capstone Partners, Industrial Bank of Korea, KB Securities, KEB Hana Bank, WOORI Venture Partners, Hana Financial Investment, KDB Industrial Bank, Sui Generis Partners, and Krew Capital.

Moreh

Moreh develops a full AI software stack. The platform enables users to optimise the life cycle of a hyper-scale AI. Moreh also provides AI infrastructure software designed for parallelisation and cluster scalability. Its software stack includes AI infrastructure and application services like chips.

Founding year: 2020
Total funding raised: US$30 million
Investors: KT, AMD, Smile Gate Investment, and Forest Partners.

Skelter Labs

Skelter Labs develops AI-based language processing technologies for enterprises. Its offerings include a conversational AI engine that allows users to build AI chatbots and provides management tools for both sequential and non-sequential conversation flows. It also provides speech recognition and synthesis technology that can be used to recognise or synthesise voices from various speakers with features like emotion, tonality, context recognition, and visual processing.

Also Read: Digital transformation and AI revolution: Shaping Singapore’s F&B industry with Korean restaurant tech

Founding year: 2015
Total funding raised: US$23.5 million
Investors: KBD Capital, Stonebridge Capital, Kakao Ventures, Korea Investment Holdings, Korea Development Bank Europe, BNK, ATP, Golden Gate Ventures, Kakao Brain, Stonebridge Ventures, LOTTE, Colopl Next, Shinhan Futures Lab, The Wells Investment, Access Ventures, and BNK Venture Capital.

Mobilint

Mobilint provides sensor fusion and deep learning systems on a chip technology, which provides accelerated artificial intelligence solutions. Its NPU solutions are optimised for AI tasks.

Founding year: 2019
Total funding raised: US$15.3 million
Investors: Kyobo Securities, Union Investment Partners, Daesung Startup Investment, Korean Development Bank, L&S Venture Capital, and FuturePlay.

AIMMO

It provides an AI model labelling and training platform. AIMMO enables users to build, train, label, analyse, and manage AI models for multiple applications. Its features include labeller analytic tools, real-time communication functions, a custom workflow designer, auto-labeling tools, a thumbnail viewer, and LiDAR data tools. The firm uses computer vision, NLP, and cloud technologies. It caters to the automotive, healthcare, and robotic industries.

Founding year: 2016
Total funding raised: US$12 million
Investors: DS, Industrial Bank of Korea, Hanwha Investment & Securities, Toss, Korea Asset Investment Securities, VentureField, and S&S Investment

Channel Talk

It provides AI- and SaaS-based customer relationship management solutions for online retailers. The platform supports retailers in capturing customers and tracking the customer identity, duration of visits, and drops in messages, offering them product recommendations, deals, and more.

Founding year: 2014
Total funding raised: US$10 million
Investors: Laguna Investment, KB Investment, Atinum Investment, BonAngels Venture Partners, Colopl Next, KDDI Open Innovation Fund, Global Brain, Korea Investment Holdings, Aju IB Investment, Stonebridge Capital, Fast Track Asia, SparkLabs, Amorepacific Ventures, Kolon Investments, IMM Investment, Fast Investment, and Four Asian Tigers.

Odd Concepts

Odd Concepts provides computer vision and deep learning-based image recognition solutions for enterprises. It uses its proprietary image recognition platform to develop products and provide cloud-based API solutions for clients.

Founding year: 2018
Total funding raised: US$10 million
Investors: KB Securities, HB Investment, Kiwoom Investment, SBI Investment Korea, Korean Development Bank, Stonebridge Capital, Colopl Next, and KB Investment.

Lablup

It is a cloud-based AI model development and deployment. Lablup develops algorithms for computing-based research using AI and cloud computing technologies. The AI firm also provides a management platform for training deep learning models and computational research, and machine learning-based chatbot, and a web UI application. The SDKs are compatible with optimised environments for machine-learning toolkits such as TensorFlow, PyTorch, and Caffe. It leverages machine learning to create apps for multiple applications such as Visual Studio Code and Atom Editor.

Founding year: 2015
Total funding raised: US$9.6 million
Investors: K2 Investment Partners, LB Investment, Industrial Bank of Korea, Daesung Startup Investment, Stonebridge Capital, Kakao Ventures, and Campus.

42Maru

42Maru provides AI, cloud, and NLP-based question-answering chatbots. The firm develops a question-answering system based on deep learning technology and NLP-based text analytics systems. The QA system is powered by a machine reading comprehension engine and paraphrasing techniques that allow computers to understand the meaning of the text and user queries and find answers to any given question. The system is applicable to digital chatbots, smartwatches, smart speakers, smart toys, connected cars, and data warehouses.

Founding year: 2015
Total funding raised: US$9.3 million
Investors: NAVER Cloud, LG U +, Hancom Secure, Hana Financial Group, Industrial Bank of Korea, Korean Development Bank, SpringCamp, and Techstars.

SOOHO

SOOHO provides blockchain-based payment security and transaction monitoring solutions. The firm offers a solution to audit smart contracts and on-chain transactions. It also provides security assessments through API and IDE plugins to find bugs, detect vulnerable codes, and verify their safety. SOOHO leverages machine learning to analyse transactions and filter unreliable ones.

Also Read: Bering Lab raises US$2.3M to take AI-powered legal translation solution beyond Korea

Founding year: 2018
Total funding raised: US$9 million
Investors: Woori Technology Investment, Samsung SDS, LG CNS, SK, Shinhan Futures Lab, and Consensys Labs

Furiosa

It provides an AI-based inference computing solution

Founding year: 2017
Total funding raised: US$6.9 million
Investors: Korean Development Bank, Truston Asset Management, Naver, DSC Investment,
NAVER D2 Startup Factory, IMM Investment, Quantum Ventures Korea, Now IB Capital, and Schmidt.

Align AI

Align is a data analytics platform that helps builders of AI-native products convert conversational data into insights. The company provides an analytics infrastructure to manage the performance of conversational interfaces and agents. It includes a natural language search tool to find specific conversations. Align leverages semantic search to identify and generate data.

Founding year: 2021
Total funding raised: US$3.5 million
Investors: KB Investment, STH, and Danal.

ACTNOVA

It is an AI vision-based behaviour test platform provider. The company offers a web application for analysing videos and leverages AI and machine learning to identify and visualise behaviours. Users can import video files by drag-and-drop and experiment with conventional behavioral apparatuses or customise.

Founding year: 2020
Total funding raised: US$2.5 million
Investors: Hana Ventures, A-Venture, and Fast Ventures.

Bering Lab

Bering Lab offers domain-specific AI translation engines to handle complex legal document translations. Its flagship translation platform is BeringAI. BeringAI+, which combines AI technology with expert review by over 500 lawyers and 800 professional translators across over 30 countries, achieves 99 per cent translation accuracy.

Founding year: 2020
Total funding raised: US$2.3 million
Investors: SBVA (formerly SoftBank Ventures Asia) and The MBA Fund.

CMITech

CMITech provides iris recognition imagers and modules for system integrators and OEMs. Its products include an iris imager for capturing and recognising to authenticate users. The solution is applied to physical access control, healthcare, civil ID, border security, and financial applications.

Founding year: 2009
Total funding raised: US$2.1 million
Investors: Magellan Technology Investment, SBI Investment Korea, Industrial Bank of Korea, and KT Investment.

Skychips

Skychips is a provider of AI-based chips for applications. It offers a data processing solution by implementing a serial data processing method known as a parallel data processing method or CNN.

Founding year: 2019
Total funding raised: US$1.67 million
Investors: SoluM, Kingospring, P&P Investment, C&Venture Partners, Daedeok Venture Partners, Korea Investment Holdings, and Genaxis.

Testworks

Testworks provides a platform known as aiWorks that specialises in training data for artificial intelligence models. It also provides learning data collection and processing solutions and claims to provide accurate data sets for the rapid growth of AI.

Founding year: 2015
Total funding raised: US$884K
Investors: D3 Jubilee Partners and Microsoft Accelerator.

Xbrain

Xbrain provides a cloud platform for automated data science functions. The platform Daria provides multiple features for data input and processing, including oversampling, scaling, and normalization. It automatically selects the best machine-learning models for each dataset from different combinations of algorithms and hyperparameters.

Founding year: 2014
Total funding raised: US$700,000
Investors: Postech Holding, Tech Incubator Program for Startup, Capstone Partners, SparkLabs.

Dabeeo

It provides an open-source and AI-enabled mapping platform. Dabeeo’s solutions include map services for location-based services, online to offline services, and custom-made styles. It also features indoor map data, API integration, map editors, POI managers, object detection, and statistic tools. The maps offer offline services, meaning they can be downloaded to the device and user.

Founding year: 2012
Total funding raised: US$445,000
Investors: LIG Nex1, SJ Investment Partners, Mirae Asset Venture, BonAngels Venture Partners, Tech Incubator Program for Startup, igniteXL, and OpenWaterINV.

Roborus

Roborus provides smart restaurant management solutions. Its smart ordering solution greets customers and recognises their orders using proprietary AI algorithms. Based on order history, Roborus also offers personalised loyalty programmes for every individual customer. It collects customer feedback and records customer satisfaction levels by analysing their facial expressions.

Founding year: 2016
Total funding raised: US$135,000
Investors: Render Capital, Metro, Techstars, Boomtown Accelerators, ActnerLab, Right Side Capital Management, Tech Incubator Program for Startup, RISE, The Farm, LeadX Capital, MXcel, and Xcel.

Weavel

Weavel offers product analytics for conversational AI products. The platform enables users to integrate the platform into conversational products. It offers pre-built reports generated automatically for actionable insights. Weavel’s features include automatically generated pre-built reports, integration into conversational products, analysis of conversations, tracking of events, and elevating data to action.

Founding year: 2023
Total funding raised: 125,000
Investors: Y Combinator and Krew Capital.

Blue Dot

It provides AI-based semiconductor IPs. Blue Dot features 4K/8K resolution and high-definition video encoder solutions that support 5G networks for live social video, cloud gaming, immersive video VR/AR, and OTT/VOD.

Founding year: 2019
Total funding raised: Undisclosed
Investors: NAVER D2 Startup Factory, KB Investment, Smile Gate Investment, and BluePoint Partners.

DoingLAB

DoingLAB provides AI-based apps for calorie tracking and nutrition recommendations. It offers DietCameraAI, an app for tracking calories and analyzing eating patterns using an AI-based camera, and Diabetic Camera, which recommends recipes suitable for diabetes.

Founding year: 2016
Total funding raised: Undisclosed
Investors: Insight Equity and NAVER D2 Startup Factory.

ZETIC.ai

It is a platform offering pipelines for serverless AI systems. It profiles and predicts AI model performance across devices and processors. The firm offers an automated pipeline for on-target AI model library implementations and supports the integration of AI services.

Founding year: 2024
Total funding raised: Undisclosed
Investors: Korea Investment Accelerator, and TheVentures.

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On 30 October, SWITCH of TAIWAN: Networking Night with Taiwanese Startups took place in collaboration with StartUP@Taipei, the Taipei Government’s program supporting Taiwanese entrepreneurs. This dynamic event gathered a diverse group of entrepreneurs, investors, and corporate representatives. As a result, the event created a vibrant platform for cross-border collaboration between Taiwan, Singapore, and Japan.

Powered by Startup Island TAIWAN, the event marked a significant step in fostering innovation across Asia. As Taiwanese startups are looking to expand beyond local markets and leverage Southeast Asia’s robust digital economy, these initiatives will help drive regional growth and collaboration.

Also read: Why Taiwan’s tech ecosystem is ASEAN’s next big growth driver

Evolving SEA startup ecosystem in focus at SWITCH of Taiwan

With around 80 participants, including representatives from 9 pioneering Taiwanese startups, the night facilitated curated discussions and networking opportunities. Investors, VCs, and CVCs benefited from a dedicated investor matching session, enabling high-impact connections with startups poised for regional expansion.

A highlight of the evening was the keynote address by Raymond Choong, Venture Partner at Focustech Ventures. Choong provided valuable insights into the evolving startup ecosystem in Southeast Asia. His perspectives on key market trends and strategies for regional growth resonated with the attendees. This is particularly true as Taiwanese entrepreneurs are increasingly seeking opportunities for expansion in Southeast Asia.

Through StartUP@Taipei’s Global Pass program, which facilitated the involvement of Taiwanese startups, Singapore is recognized as a strategic gateway for companies exploring international markets. This initiative connects startups from Taiwan to an integrated network of resources and partnerships, essential for navigating cross-border expansion.

Also read: Bridging Taiwan and Southeast Asia through innovation and tech

Fostering cross-border collaboration across Asia

The event underscored the importance of regional networking nights and investor matching sessions that enable impactful collaborations across Asia. By connecting Taiwan’s vibrant startup ecosystem with Southeast Asia’s dynamic markets, the SWITCH of TAIWAN Networking Night catalysed new opportunities for founders, investors, and ecosystem builders alike.

Further, the evening’s enthusiastic networking reinforced the need for a thriving regional ecosystem. This ecosystem is where startups can connect, learn, and grow together. As more Taiwanese startups look to scale into Southeast Asia, this event represents a vital step toward deepening relationships and building a robust entrepreneurial network across Asia. Finally, the success of this event highlights the strong demand for cross-border collaboration. As a result, it paves the way for future partnerships that will help propel startups from Taiwan and beyond onto the global stage.

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This article is sponsored by One&Co Singapore

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Startups face a handful of significant challenges in getting off the ground. While funding and breaking into the market may be the most obvious, you shouldn’t overlook your workforce, either. Turnover can be common among new businesses, and high attrition could stop you from reaching your goals efficiently.

The startup-heavy fintech sector exemplifies this issue. Singaporean fintech businesses have an average attrition rate of 10 per cent to 15 per cent, and employees typically stay with the company for just three years. Thankfully, artificial intelligence (AI) can help. Here are five ways you can use AI to reduce employee turnover:

Find ideal candidates

AI’s role in attrition reduction starts before hiring even begins. Many employees leave startups because they struggle to see the company’s long-term potential or may misunderstand its culture, as this will be less established. Consequently, you can avoid turnover by finding people who are a better fit for your business.

AI candidate screening tools can recognise subtle characteristics suggesting how well a job-seeker will match the role. Because AI is better than humans at detecting easy-to-miss trends in data, it can find ideal recruits conventional hiring approaches may overlook.

Looking for unconventional candidates can increase the available labour pool by nearly 10 times the norm. Consequently, you’re far more likely to find people who match your company culture with AI’s help.

Personalise onboarding and career development

Once you find the right employee for a role, AI can help by tailoring their experience to their requirements. Failure to nurture workers’ talents or lacking the resources to meet individual needs are other common causes of startup turnover. Automated personalisation fills that gap.

Also Read: Why building user communities is far better than paid advertising

Machine learning models can analyse an employee’s experience and preferences during recruitment to learn what they need from the company. They can then modify onboarding templates accordingly, ensuring everyone’s training matches their unique situation. Consequently, people will feel more comfortable and ready to start work at your startup.

Similar benefits apply to long-term career development. Limited opportunities for advancement are a key cause of attrition for 43 per cent of workers in Southeast Asia. You can address this need by using AI to match current employees to potential promotions or training opportunities, helping them advance while providing the most value to the company.

Automate un-engaging tasks

You can also use AI to reduce attrition by automating work your employees may find tedious or boring. As minor as such complaints may seem, low engagement leads to higher burnout and turnover, so removing unengaging tasks can have the opposite effect.

Common non-engaging work includes data entry, billing, scheduling and reporting. All of these functions are also easily automatable with today’s available AI software. AI also tend to perform such work with higher accuracy and greater efficiency, so you can save time and money by automating them, too.

Remember to use AI to complement workers, not replace them. Your takeaway should be that AI frees employees to focus on the tasks they enjoy and that contribute more to the company. Threatening job losses with automation will likely lead to worse turnover.

Evaluate employee sentiment

Another way to reduce attrition through AI is to evaluate how your employees feel about the workplace. Customer sentiment analysis is already a common AI use case in many businesses, and you can apply the same concept to your workforce to learn where company culture may need to change.

Also Read: Is omnichannel commerce a fairy tale for SMEs in Singapore?

Automated surveys and internal communications provide the data AI needs to determine employee sentiment. When using surveys, remember to ask about feelings toward your AI technology itself. It’s the second leading cause of job losses among today’s technologies, so you want to assure your workers as soon as possible if they fear such a future is headed their way.

Once your AI model summarises your employees’ state of mind, you must act. Address any common issues people bring up about the workplace and open a dialogue about how things can improve. Workers will be more likely to stay if they see positive change and witness leadership listening to their input.

Guide your startup’s future

It’s also worth considering that some employees may leave your company because they realise many startups fail. You can assuage these concerns by using AI to gain insight into your industry’s future and build a more reliable long-term strategy.

As much as 98 per cent of startups say AI is crucial to their future business strategy. You may fall behind the competition if you don’t also capitalise on the technology. Start by looking for cost or time-saving opportunities through AI-driven automation and use AI to identify inefficiencies or predict future market changes.

When you build a better business model with AI, you can assure workers of their future at a successful organisation. Attrition will fall as a result.

Startups must emphasise employee retention

Workforce turnover is expensive and disruptive. Consequently, you must boost retention to give your startup a better chance at success.

AI may not be a panacea for workforce issues, but it can assist you in many areas. Following these five steps can help you build and manage a successful, engaged team to drive your business forward.

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AI is the talk of 2024, with ChatGPT, Claude, Perplexity and other tools taking centre stage, but how will this technology transform the very fabric of some vital industries that we rely on today?

The truth is that AI is evolving so fast it is difficult to predict the long-term impact. What is clear is that it is now a supportive tool within many organisations and is quickly becoming an integral part of how companies operate. 

As AI agents become more autonomous, it is crucial that we take a step back to understand where society can benefit most. According to Capgemini research, 82 per cent of organisations with over US$1 billion in revenue plan to integrate AI agents within the next one to three years. Companies will begin to trust them with tasks like email generation, coding, and data analysis. 

Today, let’s focus on one sector that is poised for a major transition: banking and finance. With profound implications for efficiency, personalisation, and security. As these agents start transacting with one another there are profound implications for personalisation and security in the entire financial services sector.

AI agents facilitating autonomous transactions

As AI agents become more capable, they will be able to conduct transactions and complex processes without the need for human intervention. However, it is important to put the right safeguards in place to ensure the right levels of accountability when it comes to AI decision making. According to Charles Dray, Founder of Resonance Security, 

“As of today, November 2024, AI requires a human operator. Without an operator confirming accuracy of replies, and continuous AI threat modeling which tests the AI to see how much it takes to make it provide an incorrect reply and correcting it, the AI can stray away from its expected behavior. 

AI  providing incorrect replies for financial services companies can be a major risk. Not only can it damage reputation, and feed incorrect actionable information, but it will reduce the personal touch customers love. On the other hand, deploying new technology helps technology scale, so it’s a matter of who wants to dive in first. Chances are we’ll learn a lot from early adopters “

You only have to consider the scenarios to see the risks. Imagine AI agents autonomously negotiating loans, executing trades, or even processing insurance claims between different banks or financial institutions. 

Also Read: 5 common mistakes in financial modelling during startup fundraising

The autonomous nature of these transactions will mean that banks and fintech companies will be able to execute real-time financial decisions. Whether it’s approving loans based on AI-verified data or negotiating cross-border payments, the speed and accuracy of these processes will be unparalleled.

Transforming customer engagement with hyper-personalisation

AI agents excel at analysing vast amounts of data, allowing them to tailor financial services to individual customers. These agents can interact naturally with both humans and other AI systems, enabling a new level of hyper-personalisation in banking. From personalised loan offers to real-time investment advice, AI agents can cater to the unique needs and preferences of each customer.

Chris Sotraidis of Autonomys, a decentralised network designed to enable secure, sovereign collaboration between humans and artificial intelligence, notes that AI agents are already dramatically enhancing customer service by providing personalised, human like support.

“The transition has happened rapidly and is already evident. Proactive customer support is the next evolution. Chatbots, and even real-time phone bots are in beta.”

A new era of hyper-personalisation is coming and will give customers more control over their finances, allowing personal AI agents to proactively manage their savings and optimise their investments. 

AI agents revolutionising fraud detection and risk management

Traditional fraud detection systems are often slow and reactive, identifying issues after the fact. In contrast, AI agents can autonomously analyse massive volumes of transactions, quickly identifying suspicious behaviours or fraudulent activities.

For instance, Mastercard has already begun using AI to double the speed of identifying potentially compromised cards while reducing false positives. This ability to detect anomalies instantaneously allows financial institutions to act before the fraud can escalate, protecting both institutions and customers.

Sotraidis cautions against relying AI models to execute all transactions autonomously speaking about the risk of adversarial attacks on AI models where data can be easily manipulated.

“This creates a need for new governance structures to manage accountability, especially in the case of incorrect or fraudulent transactions initiated by compromised agents. It will be essential for money managers to fully understand and trust the fidelity of their decision-making systems,” says Sotraidis.

Also Read: To Voice AI or not – The changing face of customer experience

The new frontier in finance with decentralised finance (DeFi)

One of the most exciting developments today is the potential of AI agents within DeFi. DeFi platforms allow peer-to-peer financial transactions without intermediaries, and AI agents can interact with these protocols, executing trades, loans, and asset management. This opens up a future where AI agents could manage entire portfolios.

In this scenario, AI agents could even cooperate across different blockchains, managing transactions, and ensuring compliance with complex regulatory frameworks. For example, an AI agent could autonomously navigate through the DeFi ecosystem, lending assets on one platform while borrowing on another, all while managing risk dynamically based on real-time market conditions.

AI agents driving financial inclusion

In many parts of the world, access to traditional financial services is limited due to lack of adequate infrastructure. As Sotraidis points out,

“AI-driven mobile banking solutions will expand access to professional services in remote regions where in-person advisory at traditional branches is impractical.”

He explains that this helps to reduce the cost of delivering services and also enables institutions to provide tailored financial products to underbanked populations. By interacting with local economies and learning from local data, these agents can offer financial products tailored to specific needs.

It is clear that AI is continuing to play a growing role in our everyday lives and transforming traditional industries in ways that are unprecedented. However, as we embrace this new era, challenges around data privacy, transparency, and bias in AI decision-making must be addressed to ensure that these systems serve everyone fairly and responsibly.

Using decentralised finance to add a layer of transparency and immutability makes sense as we begin to allow these autonomous agents to act on our behalf.

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Vietnamese B2B food supply e-commerce platform Kamereo has secured US$7.8 million in Series B funding.

Sumitomo Corporation, Inspire Co, SMBC Venture Capital, Mitsubishi UFJ Capital, and Reazon Holdings co-led the round, which also saw unnamed investors’ participation.

This round brings the startup’s total funding to over US$15 million.

Kamereo plans to use the new capital to expand across Vietnam, starting with Hanoi. This follows its expansion into Ho Chi Minh City (HCMC), which together account for over 50 per cent of Vietnam’s GDP.

Kamereo is a wholesale food supply e-commerce firm that owns vegetable and fruit collection centres and works directly with its partners and contract farmers.

Also Read: Multifaceted effects on Vietnam’s e-commerce: A near-term potential to break through in the Asian market

The company’s marketplace business connects producers and manufacturers with its existing network of over 3,000 customers, primarily in the HORECA (hotels, restaurants, and cafes) sector. This allows suppliers to expand their sales without the need for significant upfront investments in logistics and operations. Its customer base includes restaurants, supermarkets, convenience stores, factories, schools, and hospitals.

The company has established a daily refrigerated transport network connecting the north and south. It aims to set up operations in central Vietnam to cater to the growing demand from customers beyond the two major cities.

Beyond geographical expansion, Kamereo will also focus on product diversification, introducing new services like a marketplace model and enhancing its product features.

The firm’s recent partnership with GYOMU JAPAN, the operator of Gyomu Super in Vietnam, brings approximately 450 Gyomu Super products onto the platform.

The company also plans to further develop its private label strategy, focusing on two key areas:

• Development and sale of pre-cut fruits and vegetables for supermarkets and convenience stores: This caters to the growing trend of modern trade in urban Vietnam, emphasising food safety, traceability, and health consciousness.
• Private labelling of consumables to enhance brand awareness and price competitiveness: Kamereo aims to leverage Vietnam’s OEM manufacturing capabilities to produce high-quality products at competitive prices.

Early this year, Kamereo raised US$2.1 million in a funding round co-led by Reazon Holdings, Quest Ventures, and Thoru Yamamoto (CEO of Japanese B2B seafood supply chain company FOODISON).

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Climate Tech: Seizing Opportunities in an Emerging Ecosystem

At Echelon Philippines 2024, a panel discussion titled ‘Climate Tech: Seizing Opportunities in an Emerging Ecosystem’ brought together key voices in the climate tech space to explore innovations, funding opportunities, and strategies for navigating regulatory landscapes.

Moderated by Katherine Khoo, Lead, Social Impact and Equity Action from Ayala Corporation, the panel featured insights from AC Alyzsa Dy, Head of Incubation and Venture Support at Villgro Philippines; Enzo Pinga, Head of Business Development at Humble Sustainability; and Zachary Lee, Venture Partner at The Radical Fund.

The conversation delved into critical areas of climate tech innovation, including renewable energy, sustainable agriculture, and carbon reduction technologies. Pinga shared Humble Sustainability’s circular economy model, which repurposes IT equipment to reduce e-waste. Dy highlighted Villgro Philippines’ focus on renewable energy, electric mobility, and nature-based solutions. Lee emphasised the increasing capital flow into electric vehicles while stressing the importance of concessionary capital to support early-stage startups.

Also Read: Echelon Philippines 2024: The funding landscape for Filipino startups

The panelists also addressed challenges in the sector, such as climate anxiety and the complexities of navigating carbon credit markets. They stressed the importance of inclusive climate solutions, particularly for rural communities, and the urgent need to build a robust talent pool for green jobs.

For aspiring entrepreneurs, the speakers offered valuable advice: focus on creating solutions that are not only sustainable but also commercially viable. The panel concluded with a call for collaboration across sectors to scale climate tech innovations and maximise their impact.

This discussion highlighted the growing opportunities in the climate tech ecosystem and underscored its potential to drive meaningful change in the Philippines and beyond.

Watch the session video above to learn more about these insights and the strategies shaping the future of entrepreneurship.

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Generative AI (GenAI) is reshaping industries worldwide, and ASEAN is emerging as a hotbed of innovation in this space. However, while the region’s startups are making strides in developing cutting-edge solutions, the journey from idea to market remains a formidable challenge. The ASEAN GenAI Startup Report 2024 sheds light on the unique hurdles these startups face and the strategies that can help them succeed.

Understanding the challenges

For ASEAN’s GenAI startups, particularly those focusing on B2B solutions (92 per cent of the ecosystem), the path to market is often riddled with obstacles.

Slow enterprise onboarding

Startups targeting enterprises often encounter lengthy and complex sales cycles. The process of securing contracts, which may involve tenders, validation, and Request-for-Proposal (RFP) submissions, can take months. Many enterprises require startups to demonstrate a proven operational history—an expectation difficult for younger startups to meet, especially given the nascent nature of GenAI technologies.

Cash flow constraints

Limited funding exacerbates the challenge of protracted sales cycles. According to the report, nearly half (49 per cent) of GenAI startups are bootstrapped or rely on angel funding, with only 16 per cent reporting profitability. Startups often depend on paid pilots to validate their solutions, but even when successful, payment delays of 60 to 90 days can strain their operations.

Cultural and workforce sensitivities

In ASEAN, workforce-related concerns often slow the adoption of GenAI solutions. Organisations may hesitate to embrace technologies perceived as threatening jobs. Furthermore, poorly executed proof-of-concept (POC) projects—due to misaligned expectations or inadequate data—can deter enterprises from moving forward.

Despite these challenges, ASEAN startups demonstrate remarkable adaptability. The report highlights that 75 per cent of surveyed startups have pivoted their strategies at least once to stay aligned with market demands.

Also Read: From innovation to impact: Key sectors driving GenAI adoption in ASEAN

Strategies for overcoming barriers

To navigate the GTM landscape, ASEAN GenAI startups can adopt innovative strategies that leverage their unique strengths while addressing market complexities.

Focus on niche applications

Specialisation is a key differentiator for startups in a competitive environment. By developing tailored solutions for specific industries or markets, startups can create defensible positions. For instance, Vietnam’s Mesolitica builds fine-tuned language models that cater to the linguistic and cultural needs of Southeast Asia, setting it apart from global competitors.

Build strategic partnerships

Collaborations with established players, such as cloud providers and enterprises, can accelerate a startup’s path to market. Partnerships are the most effective customer acquisition channel for ASEAN GenAI startups, with 74 per cent citing them as a critical strategy.

Cloud providers like AWS, Google Cloud, and Microsoft Azure play a vital role in supporting startups through credits, technical resources, and GTM programs. For example, ArcanicAI in Vietnam leverages AWS’s GenAI Accelerator Program to secure POCs and gain exposure to enterprise clients. These partnerships help startups overcome resource limitations and establish credibility.

Adopt a regional GTM approach

ASEAN’s diversity presents a challenge for startups but also an opportunity to expand into broader markets. By building region-specific partnerships and customising solutions for different cultural contexts, startups can scale effectively. For instance, Indonesia’s Lexilaw.ai is already running POCs across ASEAN and beyond, demonstrating how cross-border collaboration can unlock new opportunities.

Also Read: Report: New fintech talents emerge as GenAI becomes increasingly popular in Singapore

Support from the ecosystem

Governments, investors, and accelerators have a crucial role in helping ASEAN GenAI startups overcome GTM challenges.

Government-led initiatives

Policymakers across the region are launching programs to foster innovation. Singapore’s Productivity Solutions Grant and Vietnam’s National Innovation Center are examples of initiatives that provide financial support, access to resources, and opportunities for startups to showcase their capabilities.

Accelerators and cloud providers

Accelerator programs, such as AWS’s GenAI Spotlight and Google’s AI Accelerator, offer startups not only technical expertise but also market exposure and funding opportunities. These initiatives enhance startups’ ability to develop products and secure enterprise clients.

Corporate mergers and acquisitions (M&As)

As GenAI adoption grows, enterprises are increasingly interested in acquiring startups to integrate AI capabilities into their operations. Startups that align their solutions with enterprise needs are better positioned for partnerships or acquisitions.

The way forward

The GTM journey for ASEAN GenAI startups is challenging, but it is also filled with opportunities. By focusing on niche applications, forging strategic partnerships, and expanding regionally, startups can overcome barriers and achieve sustainable growth.

The role of the ecosystem—governments, accelerators, and cloud providers—is equally critical in enabling startups to thrive. With targeted support and collaborative efforts, ASEAN’s GenAI startups can establish themselves as global leaders in AI innovation.

In the fast-evolving world of GenAI, success will come to those who adapt, innovate, and leverage the collective strength of the ecosystem. The journey is complex, but for ASEAN’s startups, the rewards are worth the effort.

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This article is the third in a series from the ASEAN GenAI Startup Report 2024. GenAI Fund invests in early-stage GenAI startups across Southeast Asia, focusing on growth strategies and exit opportunities. Stay updated with new articles in this series by subscribing and following us on our channels. For more articles, visit: https://e27.co/category/reports/.

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The 2024 Olympic Games in Paris had long been acknowledged as a potential watershed moment for Airbnb (NASDAQ:ABNB). But did the homestay giant miss its opportunity to cement its position as a travel and tourism market leader? 

Airbnb’s strategy was clear, and advertising campaigns aimed to underline the firm’s USP as a provider of unique experiences that can’t be replicated elsewhere in the hospitality industry, with the tagline: ‘Why stay in a hotel on the touristy side of Paris, when you could stay in an Airbnb on the Paris-y side of Paris?’

However, one month before the Olympic Games, The Connexion reported that falling demand for Airbnb properties saw the average rate per unit drop by 32 per cent between April and June 2024. 

These falling prices come off the back of a surge in listings throughout the city, with more than 15,000 extra properties made available on short-term rental websites in the Paris area since March. 

The overall volume of visitors to Paris during the Olympic Games reached 11.2 million, but not to be outdone by Airbnb, many hotels in the city sought to drop their prices to avoid losing business. 

Airbnb’s stock slipped 16.37 per cent in Q3 2024 at a time when investors would’ve expected more optimism sparked by the games. Now, with insider sell-offs and issues with falling demand, could the Airbnb boom be facing its biggest challenge yet? 

Weaker profit as demand falls

One major contributing factor to Airbnb’s Q3 slide was the company’s weakening profit margins of US$555 million during the previous quarter compared to the $650 million reported over the same period in the year prior. 

The weakening trend saw shares in ABNB slip 12 per cent after the bell, with the company blaming a weakening market amid economic uncertainty and New York’s crackdown on homestays. 

With more Airbnb hosts implementing strict rules and higher hidden fees, the stock may have lost some of its advantages over traditional hotels, which could see more unwanted competition emerge. 

Insider sell-offs

Although insider selling isn’t necessarily a cause for concern, it’s worth noting that Airbnb insiders were net sellers over the last year. 

Airbnb co-founder Brian Chesky made the biggest insider sale within the last 12 months in a single transaction worth US$17 million. 

In total, Chesky sold 307,690 shares over the past year with the average share price weighing in at US$143. 

Also Read: ‘AIR’ review: 3 lessons for dealmaking and entrepreneurship

In October, Aristotle Balogh, the chief technology officer of Airbnb, sold 600 shares worth US$81,198 while retaining ownership of 192,244 shares in the company. 

Although insider sell-offs aren’t necessarily a sign of a struggling company, they can sometimes point to weakening confidence among stakeholders in a firm’s short-term performance. 

Add to this the recent news that Mn Services Vermogensbeheer BV opted to lessen its holdings in Airbnb by 4.5 per cent during the third quarter and a trend of sell-offs appears to be forming both inside and outside the company. Should investors be concerned?

Airbnb remains a revenue machine

Despite its short-term concerns, Airbnb remains a highly profitable innovator in the travel and tourism industry. 

“The Airbnb business model has proven extremely profitable, with the firm generating US$9.9 billion in sales in 2023, more than double its 2019 revenue before the pandemic,” highlights Maxim Manturov, head of investment research at Freedom24. 

“The company sees significant growth potential, especially in the extended stay market, where stays of 28 days or more accounted for 17 per cent of booked nights in the first quarter, likely driven by flexible work schedules in the wake of the pandemic.”

Investors can also find hope in Airbnb’s high potential ‘Experiences’ feature, which offers an entirely unique holiday experience for users that traditional hospitality firms are currently unable to emulate at scale. However, expectations have so far been tempered by the company’s inability to work out how to sell the feature on its platform. 

Also Read: HD, the Airbnb for surgeries in SEA, secures US$6M funding  

With Airbnb’s profit-to-earnings ratio expected to fall as low as 14 from current levels by the end of 2027, the prospect of adding an underpriced innovative stock is likely to attract a number of institutional and retail investors alike. 

While sell-offs have been a concern, Citi has maintained a more positive stance on Airbnb, placing a Buy rating on the stock and a price target of US$135.00. 

Life after the Olympics

Although Airbnb may have anticipated a higher pace of bookings during the Paris Olympics, there’s plenty of optimism for the homestay stock that suggests its boom period is far from over. 

Despite weakening demand and price competition from hotels, Airbnb’s revenues and innovative experience-focused pipeline suggest that the future remains bright for the stock. If ABNB remains under $150 over the short term, we may see more investors tempted to add the travel and tourism giant to their portfolios.

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In the battle between Amazon Web Services and Google Cloud, a quiet contender is silently encroaching on the battlefield. The homelab, a computing space previously reserved for the closet or garage, is now beginning to be a larger part of people’s homes and small offices. 

In my early years as a budding software engineer in the 1990s, I would often take home expired or junked office computers, quietly assembling Frankenstein’s file server. Stacks of cords, cables, and components confused my friends who would often ask why I was hoarding so much computer equipment.

The simple answer was that I loved to build private home versions of the servers I helped maintain at work. But now, an even bigger draw is pulling even non-technical people into running their own private server. 

One of the biggest current drivers for homelabs is the development of open source easily accessible machine learning algorithms. Specifically, large language models (LLMs). What was once reserved for Universities and research labs can now be run on simple hardware quickly and easily using open software. Even I have let go of my Frankenstein File Server in favour of smaller, lower-wattage single board computers that take up less space and power in my own homelab. 

How can I start my own AI homelab?

Through my journey setting up my own personal LLM, let me share the top five things you need to know in order to get started with running your own private homelab LLM.

Docker

Once a mysterious tool used by backend engineers for development and testing, Docker containers are now the backbone for beginners looking to quickly launch a machine learning application quickly and easily. A Docker container is simply a shrink wrapped package of all the software you need to run an application.

If a chef, menu, vegetables, and noodles are everything you need to make a stir fry, the Docker container version would be all these things in a box, with a simple command to start the fire, cut the vegetables, and cook the meal. 

For example, you can run your own private LLM using Docker by typing this Docker command:

docker run -d -v ollama:/root/.ollama -p 11434:11434 –name ollama ollama/ollama

Ollama

As we saw with the previous recommendation, Docker allows us to install Ollama with a single command prompt. But what does Ollama do? And why have over 5 million people downloaded it? Large Language Models come in many sizes, and using different models can be confusing to set up and configure.

Also Read: Securing tomorrow’s metaverse today: Why safety in the new frontier must leverage on hardware

Ollama provides a common interface for communicating to these LLMs using a simple application programming interface (API). This means software can be developed that “plugs into” Ollama to provide functionality, decoupled from the LLM itself. For example you can use the Ollama API in your own Jupyter Notebook to send natural language prompts to your own LLM. 

Jupyter Notebook

Almost half of all Data Scientists use Jupyter Notebooks, for good reason. Notebooks provide an easy way to both see and comment on code, and plenty of examples exist on how to use machine learning algorithms in python code, as shareable Notebooks. With a Notebook, you can easily plug into OpenAI’s ChatGPT API, for a fee.

However, if you run your own API, as shown in the above example with Ollama, you can send LLM prompts to your own homelab for privately and for free. A Notebook can be a very hands-on “learning” approach to running your own private homelab LLM. However, a more hands-off approach is also available. 

Open WebUI

If you have no interest in learning data science but just want to run your own large language model on your own private network, with minimal tinkering, Open WebUI provides an entirely self-hosted AI interface that works seamlessly with Ollama, and plenty of other LLM API services (including OpenAI’s ChatGPT).

Similarly to Ollama, the easiest way to run Open WebUI is through Docker. Once it is running, you can see the local address on your home network, and it looks and functions very similarly to OpenAI’s ChatGPT service. You even have the choice of uploading your own documents and running prompts against the text inside them.

A healthy community of developers is constantly updating functionality and features in this software in the open source community. This means you are free to download, use, and contribute as much as you like, for free. 

Single board computer

Any new modern computer can be used to run a Large Language Model, though these models run in different sizes and the computer you have may only be able to run a smaller sized one.

Also Read: Why building user communities is far better than paid advertising

The top three things that will influence how well a system fits into your homelab are the following:

• How much power does the computer consume? If you run a powerful computer running a 800+ watt power supply, be prepared for equally large sized power bills. There’s a reason many AI companies are looking into using Nuclear Power – these computers are typically very hungry for electricity and this can translate to high operating costs. Keep this in mind when you are weighing pros and cons for a big system.
• How much RAM does the computer have? Even the lowest end LLMs require at least 8GB of RAM. Some can operate with 4GB but performance will be very poor. Ideally, a system should have a lot of RAM, with 8GB minimal and 16GB substantially better. Even more will allow access to larger models. 
• Some kind of acceleration helps. This could be a GPU, NPU, or TPU. Though, to keep things simple, the best option is to find the fastest CPU within your Power (see 1.) and financial budget. In my experience, configuring machine learning algorithms to fully take advantage of acceleration is a very technical topic outside the scope of what is defined here. But if you like to spend time “tuning” your hardware to run as fast as possible, this could be a great project you can sink many hours into.

Conclusion 

Though, no matter which direction you eventually take, many options are available to customise your homelab with an increasing number of consumer centric devices. The Raspberry Pi is one of the most popular computers for homelab enthusiasts, with a low cost, low wattage, and 8GB options. The Jetson Orin is a GPU enabled single board computer, also with 8GB options though more expensive. The RapidAnalysis Darius is a low cost, low wattage Intel-based single board computer which also has an 8GB option. 

The cheapest and most accessible option is the computer you have with you at home right now. Though, most people will not want to run memory-hungry software continuously on a machine they are doing serious work on. Much like getting on a crowded runway, applications fighting for takeoff on a PC that sits right next to you, whirring its fans like a jetliner, can become annoying quickly. But there is another option. 

With so many computers heading for the junkyard daily, a little time in the “lab” can resurrect old machines into new workstations. Often, computers that struggle with Microsoft Windows are perfectly capable at running a single application in a cluster of homelab Docker containers.

For example, you can run Ollama on one e-waste machine, OpenWebUI on another separate e-waste machine, and Jupyter Notebook on a third e-waste machine, for a fully integrated homelab server cluster, and access them via a web interface locally. If you have the space, time, and patience (much like I did as a young engineer) you could slowly assemble a capable homelab using e-waste and commercially expired parts. 

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

Join us on Instagram, Facebook, X, and LinkedIn to stay connected.

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Is the Philippines the Most Underrated Market in Southeast Asia? Storehub’s Bold Bet on Its Future

At Echelon Philippines 2024, Wai Hong Fong, Chieftain and Co-Founder of StoreHub, joined Judge Calimbahin III of Endeavor Philippines for a fireside chat titled ‘Is the Philippines the Most Underrated Market in Southeast Asia? StoreHub’s Bold Bet on Its Future’. The discussion explored StoreHub’s decision to prioritise the Philippines as a key market in its Southeast Asian strategy.

Fong shared that, compared to Indonesia, the Philippines offers significant untapped potential for growth. With over 17,000 stores served across Southeast Asia, StoreHub identified the Philippines as having the lowest cost per lead and the highest willingness to pay, positioning it as a highly promising market. Despite early challenges such as bureaucratic hurdles and the necessity for a local presence, the Philippines has now become StoreHub’s fastest-growing market.

Also Read: Echelon Philippines 2024: Sabrina Tan on Lhoopa’s mission to make housing accessible

He emphasised the importance of understanding the culture and spending time in the country to build a strong foundation for business success. Fong also highlighted the evolving infrastructure in the Philippines, which supports greater opportunities for growth.

The fireside chat underscored the need for entrepreneurs to adopt a countercultural mindset and a strong hunger for success to thrive in this market. StoreHub’s bold bet on the Philippines illustrates the untapped potential in what Fong described as one of Southeast Asia’s most underrated markets, with strong prospects for growth and innovation.

Watch the session video above to learn more about these insights and the strategies shaping the future of entrepreneurship.

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