The 2025 Taiwan AI Startup Map is out! Which startups made the list? What are the 5 major trends we can see?
Image source: Provided by Zhishi
The Artificial Intelligence Technology Foundation (AIF) collaborated with Taiwan Zhiyun AI Supercomputing Accelerator to release the “2025 Taiwan AI Startup Map” on December 8. This startup map primarily includes startups established within the last eight years and evaluates them based on three core criteria: value, mastery, and importance, to outline the trends in Taiwan’s AI startup development for 2025, while also providing guidance for companies seeking transformation partners.
Note:
Value: The product or service can create new value such as new business models for enterprises or industries
Mastery: Includes the mastery of technology and data
Importance: The significance of the product or service to enterprises or industries (whether it addresses important pain points)
In addition to uncovering potential newcomers, we also value the cornerstones of the industry. The development of Taiwan’s AI ecosystem has now established a group of robust and mature enterprises. Although they are not represented in this startup map due to inclusion criteria (such as years of establishment), the technical capabilities of these seasoned service providers cannot be overlooked. AIF will continue to compile relevant lists to ensure that enterprises have the most comprehensive options when seeking transformation partners.
Image source: Provided by Zhishi
2025 AI Startup Development Trends: From Projects to Products
Since 2023, generative AI has swept across the globe, with rapid technological iterations and a surge of application tools emerging one after another. However, the vibrancy on the supply side has not directly translated into real adoption on the demand side. According to the 2025 Taiwan Industry AI Transformation Survey, nearly 70% of enterprises are still at an early awareness stage of AI, highlighting a clear gap between the speed of technological advancement and enterprises’ ability to absorb it.
Despite the slow pace of adoption, “AI anxiety” among enterprises has continued to grow, influencing the direction of the startup ecosystem. Generative AI has significantly lowered development barriers, allowing teams without deep core technologies to enter the market quickly. As a result, many products today are little more than interfaces built on top of large-model APIs. While these tools offer quick AI experiences, products lacking domain know-how often fail to integrate deeply into enterprise workflows and therefore struggle to solve real pain points. Distinguishing truly deployable technologies from hype will be a key focus of the market in the coming year.
In contrast to the past, when startups relied heavily on customized project services to accumulate industry experience, we now observe a clear shift: more teams are focusing on vertical industries and gradually transforming their expertise into standardized products. Generative AI plays a crucial role as an interface layer, using conversational interaction to lower software operation barriers for non-technical users and significantly reduce learning curves and adoption thresholds.
Notably, two key trends stand out:
1. The Advantage of Domain Experts
Founders with deep industry backgrounds are better positioned to identify real pain points. They can precisely determine where AI intervention is most effective and develop solutions that truly meet industry needs.
A representative example is Dachan Group, a leader in Taiwan’s agriculture and food industry, which incubated the AI restaurant service brand Daneng Information. Leveraging extensive operational data across farming, processing, and food services, it applies AI to optimize production, logistics, and decision-making—addressing inventory management, waste control, and complex ordering processes for SMEs and food retail businesses.
2. The Leverage of Existing Assets
Startups with accumulated technology and data assets show stronger adaptability to the generative AI wave. They can rapidly integrate GenAI into mature product architectures and reinforce their competitive advantages with long-term industry data, creating technology moats that are difficult to replicate.
Three AI Startups of the Year
Software–Hardware Integration as a Technology Moat: Rayleigh Vision Intelligence (RVi)
RVi focuses on AI-enabled MicroLED smart manufacturing and is a key practitioner of AI-assisted mass optical component transfer. With a team evenly split between software and hardware engineers, it combines academic innovation with industrial execution.
MicroLED mass transfer involves millions of micro-chips and remains a major bottleneck due to low yield and inefficiency. RVi’s proprietary AI algorithms address chip defect screening, transfer damage, and post-transfer repair—significantly improving yield and accelerating large-scale production.
Innovative Applications Through Technology: iSunFA Intelligent Accounting
iSunFA.com is an innovative cloud accounting platform reshaping financial workflows through AI. It automates accounting tasks and serves as an intelligent matching hub connecting businesses with professional accountants.
Using homomorphic encryption, blockchain, and zero-knowledge proofs, the platform enables real-time reporting with strong privacy protection and tamper resistance—greatly enhancing audit efficiency, trust, and fraud prevention.
Breaking Data Silos, Enabling AI Deployment: IsCoolLab
Founded in 2018, IsCoolLab focuses on industrial-grade RPA and launched Robotiive, an automation platform integrating AI and computer vision.
Robotiive supports non-intrusive deployment, enabling cross-system automation without modifying legacy code. It helps enterprises overcome data silos and has become a key partner for AI-driven automation in manufacturing and finance.
Five Key Trends in Taiwan’s AI Development
The Rise—and Risks—of Agentic AI
Moving beyond “RAG + Chat” toward task execution via APIs will define product value. However, over-empowering AI agents also raises serious cybersecurity risks.Software–Hardware Integration in Vertical Industries (e.g., Robotics)
Taiwan’s strength lies in combining AI software, domain know-how, and edge hardware—an advantage difficult for global giants to replicate.Growing Demand for Cloud–Edge Integration
Edge AI and small language models (SLMs) enable low-latency, secure, on-premise deployment—critical for manufacturing and healthcare.Dual-Track Open and Closed Models: Synergy of LLMs and SLMs
While LLMs dominate core technology, SLMs excel in vertical applications. Hybrid architectures balancing cost, performance, and privacy will be key.Scenario-Driven AI: Cities as Ecosystems
Cities will become testbeds for AI ecosystem collaboration, accelerating real-world deployment through focused vertical scenarios.
Core takeaway: application-driven, vertically integrated solutions will define the next stage of AI. Startups with strong domain expertise and accumulated assets will build durable moats in a rapidly evolving landscape.
Reproduced with permission from Knowing Trends
Author: Yang Yu-Ching
Original title: “2025 AI Startup Map Released! Vertical Integration Will Be the Key”