Top-10 Artificial Intelligence Startups in Singapore
Contrary to what many Americans will try to tell you, Singapore is not a city in China, lah. It’s an island city-state off southern Malaysia that is not only a wealthy high-tech manufacturing hub with the seventh-highest GDP per capita in the world, but it’s also home to the 12th largest startup ecosystem in the world. Thanks to government incentives and support, the ease of doing business, and one of the lowest crime rates in the world (some shops don’t even bother locking up,) it’s been a regional center for startups and tech companies since the late 1990s.
With about the same population as Colorado but with far less cannabis for sale, the tiny country of Singapore is not sitting on its laurels and released an AI strategy in 2017, a five-year, $110 million national program to enhance the country’s AI capabilities. The government-wide partnership involves six different organizations and aims to invest in the next wave of AI research, addressing social and economic challenges, and broadening the use of AI within various industries. We queried Crunchbase to find the 10 most funded AI startups in Singapore.
|AnyMind Group||Marketing, HR||27.9|
|Ocean Protocol||Big Data||22.1|
|AND Global Pte.Ltd||FinTech||16.5|
AI for Inventory Management
We previously looked at Trax’s image recognition technology in our 2017 article on “11 Examples of Grocery Store Technology.” The startup, founded in 2010, has increased its total funding to $286.7 million since then, and acquired San Francisco retail intelligence startup Quri in early 2018. Trax’s platform uses image input from mobile devices, stationary cameras, and robots to identify and map products on shelves in retail stores. Digitized shelf data is then analyzed for real-time insights on display strategy, brand share, distribution, and items out of stock.
According to Trax, $634 billion is lost in sales every year globally due to inadequate stock management, and retailers and manufacturers can achieve a 3-5% sales uplift with improved in-store execution. The company also offers IoT smart coolers keeping track of stock levels and price, monitoring store logistics without the need for human intervention. The startup’s clients include global names like Coca Cola and Henkel.
Update 07/29/19: Trax has raised $100 million in Series D funding to further support the global expansion of the company and accelerate mass-market deployment of its retail solutions. This brings the company’s total funding to $386.9 million to date.
It’s Like HR But it Adds Value
Founded in 2013, CXA Group has raised $33 million to develop a corporate health benefits aggregator, not unlike a TripAdvisor for insurance, healthcare, and wellness. The platform’s machine learning and predictive analytics algorithms use biometric data, health risk assessment questionnaires, and medical expenditures to segment employees according to cost contribution, highlight the drivers of healthcare spending, and improve preventive measures for at-risk employees like gym memberships or quit-smoking programs.
According to an eight-week case study conducted by CXA, 43% of employees reported improved happiness, and the client projected savings of $289 per annum per employee due to increased productivity. (We prefer the zero-cost incentive plan we adopted here at Nanalyze. Work or get fired.) CXA’s platform provides personalized benefits for clients’ employees instead of a cookie-cutter approach, and the solution is presently being offered in Singapore, Malaysia, Hong Kong, Indonesia, and China.
Update 03/13/19: CXA Group has taken in $25 million in Series C funding for expansion in Asia and later into Europe and North America. This brings total funding to $58 million to date.
AI Jack of All Trades
Founded in 2016, AnyMind Group has raised $27.9 million to develop AI solutions for advertising, marketing, and human resources, with each division run by separate subsidiaries. AdAsia Holdings offers marketers and publishers a machine learning platform to plan, predict, optimize, and run digital advertising activities. CastingAsia is an influencer platform utilizing computer vision, deep learning and Natural Language Processing (NLP) to match influencers with brands, manage campaigns, and report results. TalentMind does the same thing for recruiters, tapping into prospective employees’ digital data to understand their behavior and personality traits, competencies, and skills above and beyond the traditional resume. The group, funded by Japanese messaging app Line and the Mirai Creation Fund backed by Toyota, has 13 offices in the Asia Pacific region and is planning an IPO soon.
Update 03/22/19: AnyMind has raised $8 million in new Series B funding to support further vertical expansions moving into outdoor advertising in Thailand. This brings total funding to $35.9 million to date.
Real-Time Consumer Data
Founded in 2012, Near has raised $25.5 million from the likes of Sequoia Capital, JP Morgan, and Cisco to develop a real-time consumer data platform for analytics and marketing purposes. The company’s Ambient Intelligence Platform uses human mobility data from 1.6 billion users, combines it with enterprise and consumer data from diverse sources, and runs machine learning algorithms to generate business insights. It provides granular information on consumers’ interests, activities, geography, and behavior using a single unique identifier that keeps everything anonymous.
Near’s Allspark analytical engine runs queries on this intelligence platform allowing audience segmentation, spatial analytics, and omnichannel activation across 21 countries and is used by one in five Forbes Top-100 brands including Volkswagen, Unilever, and Google.
Can You Hear Me Now?
Founded in 2012, Cellwize has raised $24.5 million to develop Self-Organizing Network (SON) solutions for mobile network operators. The platform autonomously operates and optimizes mobile networks, and does so using existing infrastructure without the need to change technologies or adjust to different network service providers. Networks are sensitive and can become unstable very fast and unexpectedly, and most of the work required to keep a network stable can be automated.
Cellwize’s solution promises cell providers 10% improved coverage, a 25% increase in network capacity, and a 30% increase in data throughput. The company’s SONStudio network command center automates routine tasks and gives real-time control to operators to intervene across multi-carrier, multi-vendor, multi-technology networks as necessary. PolicyStudio creates and maintains network design using a collaborative workflow engine and provides testing environments for pre-production. The startup has also developed a crowdsourced analytics framework for network monitoring and optimization, which also includes a car connectivity solution called Autopia as well. Cellwize is presently working on projects in North America, Latin America, Europe, and APAC.
Your Data, Our Tokens
Founded in 2017, Ocean Protocol has raised $22.1 million in an Initial Coin Offering (ICO) subscribed to by professional blockchain and cryptocurrency investors to develop a decentralized data exchange for AI applications. The framework allows for traceable, transparent, and private data transfer between data providers and consumers, using a service layer powered by cryptocurrency tokens. Besides data, the exchange has storage, computational services, and algorithms as well, and provides a framework for licensing and pricing. Ocean Protocol is accepting data marketplaces who want to join, and only provides the framework for data and service transfer, ensuring data owners control their own data sets and cannot be locked into one single marketplace.
The company proceeded with the ocean token pre-launch and has set up a test network already, with a live network launch planned in Q1 this year. The startup is supported by a Singapore-based non-profit foundation, whose mandate is to ensure open access to the protocol and platform, provide data governance, encourage the network’s growth, and ensure the platform’s decentralized nature. Hopefully, the people who buy the tokens receive equity otherwise that’s a pretty daft “investment” to make.
Selling Everyone, Everything
Founded in 2010, Eyeota has raised $20.5 million in funding to develop a consumer big data platform building different “audiences” based on what people are buying, watching, reading, and listening to in both the digital and the offline world. The startup sells access to its four billion audience profiles and 25 thousand audience segments to marketers and brands running targeted campaigns, and also creates custom audience profiles on demand. Data is obtained directly from data owners like publishers and market researchers, anonymized, and analyzed using proprietary algorithms. Eyeota is directly integrated with an extensive network of ad buying platforms, trading desks, and ad networks for the distribution of audience data with a presence in Europe, Asia, and the Americas.
A New Credit Score
DemystData is a consumer data platform we first came across a few years ago in an article titled “AI, Big Data, and Your New Credit Score.” Since that article, the company has raised another $7 million bringing their total funding to $19 million. DemystData targets retail companies, insurers, and lenders to help them determine customer credit potential using big data including demographics, geospatial data, and social media. Combining 258 commercial data products from large providers like Experian and emerging players like HouseCanary, the platform is able to do credit scoring, look at geospatial locations for fraud detection, and qualify individuals as potential customers for products, just to name three of their many use cases. The startup offers an Application Programming Interface (API) to developers allowing the integration of data feeds into other systems for transformation or data development.
Another New Credit Score
Founded in 2015, AND Global has raised $16.5 million to create a FinTech solution for underbanked customers in emerging markets using AI for credit scoring, and blockchain technology for the payment system. Traditional credit scoring is based on a person’s previous credit and banking history, hence many people without bank accounts get excluded. AND Global’s algorithms use non-conventional data sources including behavioral data to determine an individual’s creditworthiness.
The startup’s personal lending app called “LendMN” is available in app stores already, disbursing more than 2,500 loans per day with a repayment rate of 98.5% and a month-on-month loan growth rate of 70%. The company is planning to expand to Japan, Indonesia, and Pakistan next, then develop solutions for blockchain-based microfinancing, biometrics-based payment systems, and a cryptocurrency. Sounds similar to the work HARA is doing with Indonesian farmers.
Cognitive Machine Reading
Founded in 2015, AntWorks has raised $15 million to develop a suite of data, machine learning, and automation solutions for business applications. The startup’s “cognitive machine reading” platform ingests structured, unstructured, inferred, and image data and prepares the data for consumption by machine learning algorithms. The enterprise intelligence platform provides different machine learning algorithms for various business scenarios.
For example, the company’s “Softbots” are used to automate internal processes. A typical example of this workflow is the need for a professional services company to edit 401K details for thousands of employees who all work at different firms. The employees simply need to send supporting documentation in an email, and the AntWorks’ algorithms identify and read the documents, then proceed to process them and carry out the requested changes. The startup has partnered with a handful of vendors delivering and implementing their services, including IBM and Oracle.
Singapore’s top 10 AI startups have received a significant amount of funding, $491 million in total, surpassing much larger markets like Germany. Applications are dominated by big data technologies and marketing solutions, and most of these well-funded startups are already making names for themselves on the global market. Originality wasn’t lacking, and we even came across an “AI” flatbread maker home appliance that took in $48.5 million – a less than core application of machine learning – which we excluded from the list. As much as we think AI has potential, you can’t convince us that AI can do roti better than your average Indian didima. Home appliances aside, with its hospitable business environment and thorough AI strategy, Singapore seems well equipped to take part in the global AI race.
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