Durable
Nima Keivan has diverse work experience spanning over several years. Nima is currently serving as the CEO and Co-Founder of Durable since 2022. Before this, they worked as a Venture Partner at Xplorer Capital, an investment firm focused on supporting entrepreneurs and groundbreaking technologies. Nima also held the position of Principal at Amazon, where they contributed to the development of autonomous robotics. Prior to that, they co-founded and served as the CTO of CANVAS Technology, a company specializing in autonomous mobility for warehousing and manufacturing. Nima's early career includes roles as a Research Assistant at the University of Colorado Boulder and George Washington University. Additionally, they co-founded Project Andromeda and held positions as a Senior Software Developer at SPARQ Solutions and Software Developer at Drawpoint Software.
Nima Keivan completed a Doctor of Philosophy (PhD) in Computer Science from the University of Colorado Boulder in 2017. Prior to that, from 2005 to 2009, Nima studied Mechanical and Space Engineering with a focus on Engineering and Physics at The University of Queensland.
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Durable
We’re on a mission to transform access to custom software using explainable AI capable of human-level reasoning and dialogue. We envision a future where custom, flexible, and durable software is democratized and accessible to everyone. We are a VC-funded startup founded by repeat founders, building a product in a new category. Realizing this vision requires AI that reasons over and continuously learns an unbounded and customized knowledge base. It requires AI that, when given a task, determines missing information, asks clarifying questions, and highlights assumptions. It requires AI that explains its reasoning and validates its answers. We don’t see evidence that the current paradigm of ever-larger deep learning language models can realize this vision. So our approach combines the strengths of deep learning in dealing with noisy and ambiguous data with the strengths of symbolic AI in explainable reasoning and data-efficient learning. We believe that this neuro-symbolic flavor of AI will enable more useful and reliable applications in the long term. If, like us, you are skeptical of the status quo and are excited to develop the next chapter of AI in a product-focused team, reach out to us or check out our open positions.