QuanTuring Selected for the TAI1 AI Accelerator
QuanTuring Inc. has been selected for the second cohort of the TAI1 AI Accelerator, which kicked off on September 1, 2026. Our thanks to StarFab and the selection committee.
TAI1 is Taiwan's first AI accelerator, run by StarFab — Taiwan's largest corporate accelerator — with resources from the NVIDIA Inception program. It focuses on manufacturing, healthcare, robotics, retail, and logistics, connecting startups with Taiwan's leading industries to accelerate product validation and deployment.
QuanTuring is a member of the NVIDIA Inception Program. We build the cognitive layer for Physical AI: QuanCog, an audit-grade enterprise knowledge platform where every answer traces back to its source document and page — and the system declines to answer when it cannot find grounds. The engine reaches 97.4% Hit@5 on a 200-question internal benchmark (Wilson 95% CI), with 100% retrieval in Chinese and Japanese, and runs from cloud to fully air-gapped on-premise deployment.
Why TAI1
Manufacturing is QuanCog's home ground. Equipment manuals, process documentation, regulatory and audit records — these are exactly the settings where every answer must carry its source. The industries TAI1 connects to are where we want audit-grade AI to work.
Through the acceleration program, we will deepen field validation with leading enterprises and continue to draw on NVIDIA Inception's technical resources, bringing the performance advantage of local inference (7.3× with NVIDIA NIM over Ollama, NVIDIA GeForce RTX 5090, 870 data points; published on the NVIDIA Developer Forum, 31 March 2026) to more production floors.
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An enterprise cognitive layer that cites, refuses, and runs from cloud to fully air-gapped.
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QuanTuring Inc. positions itself as the Cognitive Layer of Physical AI and builds QuanCog, an audit-grade enterprise knowledge platform. Every answer cites the exact source document and page; high-stakes questions go through a multi-model council review where AI reviewers cross-check each other's citations before release; and the system refuses to answer when the corpus lacks evidence. The same engine deploys on cloud, hybrid, or fully air-gapped on-premise environments — because in regulated manufacturing, semiconductor and financial settings, data cannot leave the plant. The self-built retrieval engine achieves 97.4% Hit@5 on a 200-question benchmark (Wilson 95% CI), with 100% retrieval on Chinese and Japanese corpora and 0.945 cross-domain MRR. On-premise full-stack inference performance is published on the NVIDIA Developer Forum. A member of the NVIDIA Inception Program.
Learn More: https://quanturing.ai
Media Contact: ask@quanturing.ai