Dexterity AI vs Preferred Networks

Dexterity AI has a disclosed valuation of $1.7B, while Preferred Networks's valuation is not publicly available, making a direct size comparison difficult. Dexterity AI employs 150 people.

Preferred Networks has raised more in total funding at $350M, compared to Dexterity AI's $300M — a gap of $50M. Combined, the two companies have completed 4 known funding rounds.

Both Dexterity AI and Preferred Networks share the same Awaira Score of 72/100. The Awaira Score is a composite metric that factors in valuation, total funding raised, company stage, employee count, and market category.

Dexterity AI was founded by Samir Menon in 2017. Preferred Networks was founded by Toru Nishikawa in 2014. Visit each company's profile on Awaira for a full founder biography.

Dexterity AI: Dexterity AI develops artificial intelligence and robotic systems for warehouse automation and logistics operations. Founded in 2017, the company specializes in computer vision and machine learning technologies that enable robots to perform complex manipulation tasks, particularly in e-commerce fulfillment and material handling environments. The company's core technology focuses on perception systems and AI algorithms that allow robotic arms to identify, grasp, and sort items with varying shapes, sizes, and materials—tasks traditionally requiring human workers. Dexterity AI has raised $300 million in total funding and achieved a $1.6 billion valuation, indicating strong investor confidence in the logistics automation sector. The company operates at Series C stage, positioning it among mature venture-backed robotics firms competing in a growing market for warehouse automation solutions. Its technology addresses labor shortages and operational efficiency challenges facing large-scale logistics operations, with applications extending across e-commerce fulfillment, parcel sorting, and supply chain optimization. The company competes with other robotics and automation firms targeting similar warehouse use cases. Dexterity AI's growth trajectory reflects broader industry trends toward increased automation adoption in logistics and supply chain sectors. The firm's focus on practical, deployable automation solutions for real-world warehouse challenges differentiates its approach from more experimental robotics research initiatives. Dexterity AI combines advanced computer vision with robotic manipulation to automate unstructured tasks in logistics that competitors have historically struggled to solve. Preferred Networks: Preferred Networks develops deep learning technology applied to robotics, autonomous driving, and industrial applications, building neural network architectures for real-time edge inference in robot control, factory automation, and connected vehicle systems. The Tokyo company gained international recognition for early competition victories in autonomous navigation and robot manipulation challenges and developed its own deep learning framework, Chainer, which influenced the design of PyTorch before Chainer was eventually retired.\n\nThe company raised approximately $350 million including a landmark $105 million Series A from Toyota Motor Corporation and other strategic investors, making it one of the most valuable AI startups in Japan at the time of its fundraising. Preferred Networks collaborates with Toyota on autonomous driving AI, with NTT on communications AI, and with Fanuc on factory robot intelligence, creating a portfolio of deep technology industrial partnerships that provide both funding and deployment scale for its AI research.\n\nPreferred Networks operates in Japan industrial AI market where established relationships with major manufacturing and automotive companies provide a defensible position that international AI startups find difficult to penetrate through conventional sales approaches. The company research focus on edge AI inference for robotics aligns with Japan competitive strengths in manufacturing automation and precision robotics, markets where AI-enhanced robot intelligence is being adopted to address labour shortages and quality requirements that purely mechanical automation cannot satisfy.

Preferred Networks was founded first in 2014, giving it 3 years of additional market experience. Dexterity AI was founded later in 2017. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Dexterity AI has approximately 150 employees, while Preferred Networks has approximately 100-500. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Dexterity AI and Preferred Networks are direct competitors — both operate in the AI Robotics space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.