Baraja vs Preferred Networks

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Baraja employs 100-500 people, while Preferred Networks has 100-500 employees.

Preferred Networks has raised more in total funding at $350M, compared to Baraja's $32M — a gap of $318M.

Preferred Networks holds the higher Awaira Score at 72/100, compared to Baraja's 50/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 22-point gap that reflects meaningful differences in scale or traction.

Baraja was founded by Federico Collarte in 2016. Preferred Networks was founded by Toru Nishikawa in 2014. Visit each company's profile on Awaira for a full founder biography.

Baraja: Baraja develops a novel lidar technology called Spectrum-Scan that steers laser beams using the natural dispersion of light through a prism rather than mechanical rotating mirrors or solid-state optical phased arrays, producing a solid-state lidar architecture that is more reliable and cost-effective than mechanical lidar alternatives for autonomous vehicle and robotics applications. The Sydney company holds fundamental patents on the Spectrum-Scan approach that provide IP protection across the lidar market.\n\nThe company raised approximately $32 million in venture funding including a Series B from investors including Main Sequence Ventures, Blackbird Ventures, and the CSIRO Innovation Fund. Baraja has engaged with automotive OEMs and autonomous vehicle companies in the United States and Asia for sensor evaluation, with the Spectrum-Scan lidar offering adjustable range and resolution properties that allow the sensor field of view to be customised for different driving scenarios.\n\nBaraja competes in the automotive lidar market against Velodyne, Luminar, Ouster, and Innoviz, a market that has seen significant consolidation and several company failures as autonomous vehicle development timelines extended and procurement volume projections were revised downward. Its IP position around the Spectrum-Scan technology provides a differentiation that mechanical lidar alternatives cannot access, and the solid-state reliability argument is increasingly relevant for automotive customers requiring the sensor lifetime and production scalability that mechanically rotating lidar systems cannot guarantee. 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 2 years of additional market experience. Baraja was founded later in 2016. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Both Baraja and Preferred Networks report similar employee counts of approximately 100-500. Team size is often a proxy for operational scale, though lean AI companies can punch well above their headcount.

Yes, Baraja 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.