Shakudo

Shakudo provides an operating system for data and artificial intelligence stacks, enabling enterprise data science and machine learning teams to build, deploy, and manage AI products. The platform is designed to reduce complexity and engineering overhead by creating compatibility across a wide array of best-of-breed data tools, allowing clients to avoid vendor lock-in.

Founded in Toronto, Canada, in 2021 by Yevgeniy Vahlis (CEO), Stella Wu (VP of Technology), and Christine Yuen (Head of Product), the company originated as a side project called DevSentient. The founding team possesses extensive experience in applied AI and machine learning from their time at institutions like the Bank of Montreal (BMO), Borealis AI, Georgian Partners, and Deloitte. Vahlis, for instance, previously built applied AI and ML product groups at BMO and Georgian Partners. The company rebranded to Shakudo in late 2021.

Shakudo’s core product is a platform that automates DevOps and simplifies the data stack, allowing teams to focus on developing and deploying AI solutions more rapidly. It integrates over 170 commercial and open-source tools, including those for generative AI like LangChain and ChromaDB. The system can be self-hosted within a client's own virtual private cloud, which ensures data security and privacy. The business model is based on providing this end-to-end platform to clients, which include organizations in finance, healthcare, insurance, and defense. Use cases have included deploying large language models (LLMs) to summarize research, detecting weapons in security feeds, and forecasting energy usage. Since launching its product, the company has secured significant funding, including a $4.2 million CAD seed round in 2021 and a $7.2 million USD Series A round in 2023, bringing its total funding to over $11 million.

Keywords: data stack operating system, MLOps platform, AI deployment, machine learning infrastructure, data engineering automation, generative AI, automated DevOps, enterprise AI, data stack integration, model serving, data pipelines, cloud cost optimization, AI application development