AltaML Explores U.S. Expansion
AltaML, a leading applied artificial intelligence (AI) company, is pleased to announce its expansion into the U.S. market, which opens up key opportunities for growth. AltaML selected Houston, TX, for its first U.S. location after developing a proven approach to applied AI that delivers value to clients and hyperscale partners.
“The U.S. has always been part of the plan for AltaML. We are driven by a mindset to scale as much as we are driven by a purpose to elevate human potential with applied AI,” said Co-Founder and Co-CEO Nicole Janssen. “We are feeling ready to walk the walk, starting in Houston.”
AltaML has set up a U.S. entity to enable local hiring, and to conduct business from the Texas office. Initial hiring will be focused on a small business development and support team, with additional recruiting once sales traction is established.
Houston was selected based on AltaML’s experience in the primary industry of energy and natural resources as well as connections through existing clients and partners. Texas’ strong AI and machine learning academic institutions also figured prominently in the selection process.
“This is a big step for AltaML, but it’s also a natural one,” said Co-Founder and Co-CEO Cory Janssen. “We have already worked with U.S. clients and we know that our business model resonates with the U.S. market. After a lot of careful consideration we’re excited to be embarking on this big growth opportunity.”
Headquartered in Edmonton with offices located in Calgary, Toronto and Waterloo, Houston will mark AltaML’s first U.S. office and fifth office in North America.
AltaML is a leading developer of artificial intelligence (AI)-powered solutions. Working with organizations that want to use AI to leverage their data to develop solutions that drive tangible business results, AltaML empowers partners to create operational efficiency, reduce risks and generate new sources of revenue. Through a deep understanding of organizational pain points and challenges, AltaML’s solutions encompass the entire machine learning (ML) life cycle, from evaluating potential use cases and determining feasibility to piloting solutions, putting code into production, and ensuring model evolution.