BorealisAI vs Silo AI

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. BorealisAI employs 100-500 people, while Silo AI has 100-500 employees.

Silo AI has raised $60M in disclosed funding across 0 known rounds. BorealisAI's funding history is not publicly available.

Silo AI holds the higher Awaira Score at 60/100, compared to BorealisAI's 55/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 5-point gap that reflects meaningful differences in scale or traction.

BorealisAI was founded by Russ Salakhutdinov in 2018. Silo AI was founded by Peter Sarlin in 2017. Visit each company's profile on Awaira for a full founder biography.

BorealisAI: Borealis AI is the AI research laboratory of Royal Bank of Canada, conducting fundamental and applied machine learning research with a focus on financial AI applications including time series forecasting, anomaly detection, natural language processing for financial documents, and AI fairness in credit and fraud decision systems. The Toronto-based lab collaborates with academic institutions and publishes research while developing AI capabilities deployed across RBC banking and capital markets operations.\n\nFully funded by Royal Bank of Canada, one of Canada largest financial institutions, Borealis AI operates without external investment, accessing the capital resources and data infrastructure of a tier-one bank for its research programmes. The lab has published extensively in top machine learning venues and developed open-source tools for temporal point processes and probabilistic forecasting that have been adopted by the broader research community.\n\nBorealis AI competes for talent with AI research labs including Vector Institute, Mila, and commercial labs from Google DeepMind and Meta AI that recruit from the same Canadian academic AI talent pool. The RBC affiliation provides access to real financial data at scale that academic labs cannot access, enabling research on problems including financial time series prediction and credit decision fairness that require production-scale data. The lab serves as both a research institution and a talent pipeline for RBC internal AI engineering teams building AI features across the bank retail, wealth management, and capital markets business lines. Silo AI: Silo AI operated as a private European AI lab providing foundation model development, applied AI research, and AI product engineering services to enterprise clients across Europe, building custom AI systems and adapting open-source foundation models for domain-specific applications in manufacturing, healthcare, and financial services. The Helsinki company combined academic research partnerships with commercial AI deployment expertise.\n\nThe company raised approximately $60 million in venture funding before being acquired by AMD in July 2024 for approximately $665 million, one of the largest acquisitions of a European AI company. The acquisition gave AMD an AI software engineering team to develop AI software and tools optimised for AMD Instinct GPU hardware, addressing the critical software ecosystem gap that AMD faces in competing against NVIDIA CUDA in the AI training and inference market.\n\nSilo AI competed in the European AI services and research market alongside AI labs and consultancies, but its acquisition by AMD represented a strategic uplift from commercial AI services to chip ecosystem development. The acquisition price reflected AMD valuation of Silo AI engineering talent and AI model optimisation expertise rather than its revenue run rate, indicating the premium that semiconductor companies are willing to pay to close software ecosystem gaps against NVIDIA dominance. Post-acquisition, the Silo AI team focuses on optimising AI frameworks, model libraries, and developer tools for AMD AI accelerator hardware.

Silo AI was founded first in 2017, giving it 1 year of additional market experience. BorealisAI was founded later in 2018. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Both BorealisAI and Silo AI 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, BorealisAI and Silo AI are direct competitors — both operate in the Foundation Models space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.