Database

Startups

Main Industry
Artificial Intelligence (AI)
Main Product/Service
Nilvana AI, Computer Vision AI Development Tools, Model Optimization and Deployment Tools
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Founded Year
2015
Unified Business No.
24720916
Status
Active
Number of Employees
4
Total Paid-in Capital
250,000,000 (NT$)
Location of Company
Taiwan , New Taipei City
Company names, registration status, legal representatives, founding years, paid-in capital, registered addresses, and other details are sourced from business registration records maintained by Taiwan’s Administration of Commerce, Ministry of Economic Affairs. Exit status includes Emerging Stock Board listings, M&A, and IPOs. Information on overseas expansion is self-reported by startups. Note 1: FINDIT compiles company information from public and other sources for reference and does not serve as a point of contact for the companies listed. Note 2: Some company profiles and product/service descriptions are translated using AI and provided for reference only. In case of discrepancies or ambiguity, the original text prevails.
About the Company
INWINSTACK INC was established in 2015. We take Sincerity (True), Accountability, and Contribution as the core values and philosophy of our company's operations, pursuing sustainable corporate management and growth. Founded in 2020, Nilvana features high-quality hardware, flexible and rich AI frameworks and software, and is co-created by a professional team.

We build Nilvana AI workstations/systems (workstation) + smart edge devices (Edge), providing easy-to-use AI tools and GUI interfaces, as well as rigorously tested, stable, and aesthetically pleasing hardware to meet various AI training and application inference needs. Based on years of accumulated experience in the fields of AI, IoT, and cloud technology, Nilvana will transition from industry into daily life. Its product applications span digital life industries such as digital transformation, IoT industrial/commercial applications, education, catering, and retail, and can assist you in addressing areas such as AI data (Big Data), AI model training (Model Training)/deployment (Model Deployment), machine learning (Machine Learning), deep learning (Deep Learning), object classification (Object Classification)/image recognition (Object Detection), and other topics.


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