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SG physical AI startup TacnIQ.ai raises $1.5m

TacnIQ.ai said it has collected more than 5,000 hours of tactile interaction data from deployments and controlled experiments.

SG physical AI startup TacnIQ.ai raises $1.5m

The artificial intelligence startup TacnIQ.ai has secured US$1.5 million in seed funding to develop technology that allows machines to interpret touch and physical interaction. The investment, led by Singapore-based In Group Holdings, will be used to create tactile AI foundation models, expand the engineering team, and scale commercial applications in various industries such as logistics, construction, e-commerce, hospitality, and healthcare.

Unlike many AI companies that start with software and seek real-world applications later, TacnIQ.ai has already deployed its technology in operational environments using sensor-based data collection nodes. These nodes gather tactile signals and support workplace safety and ergonomics applications, providing a practical entry point into sectors where physical work remains challenging to automate and measure.

The company's CEO, Aashish Mehta, emphasized that the investment will help hire engineers, develop more sophisticated tactile models, and turn the technology into scalable industrial solutions. Tactile AI is crucial because it enables machines to understand pressure, vibration, friction, impact, movement, and resistance—information that is harder to capture, label, and model compared to digital data.

With over 5,000 hours of tactile data collected from controlled experiments and commercial deployments, TacnIQ.ai boasts one of the world's largest tactile datasets. This large-scale, diverse, and real-world data is essential for training foundation models that can generalize across multiple tasks and conditions. By leveraging commercial deployments in various sectors, TacnIQ.ai aims to create a robust model capable of adapting to diverse applications without the need for separate models for each use case.

Written by urgent.news from e27's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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