Continual Launches With $4 Million in Seed to Bring AI to the Modern Data Stack

Continual, a company building a next-generation AI platform for the modern data stack, today announces its public beta launch with $4 million in seed funding. The round was led by Amplify Partners, a firm that invests in companies with a vision of transforming infrastructure and machine intelligence tools. Illuminate Ventures, Essence, Wayfinder, and Data Community Fund also participated in the round.

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“Getting continually improving predictive insights from data is critical for businesses to operate efficiently and better serve their customers. Yet operationalizing AI remains a challenge for all but the most sophisticated companies”

The modern data stack centered on cloud data warehouses like Snowflake is rapidly democratizing data and analytics, but deploying AI at scale into business operations, products, or services remains a challenge for most companies. Powered by a declarative approach to operational AI and end-to-end automation, Continual enables modern data and analytics teams to build continually improving machine learning models directly on their cloud data warehouse without complex engineering.

Continual brings together second time founders Tristan Zajonc and Tyler Kohn who previously built and sold machine learning infrastructure startups. Cofounder and CEO Tristan’s first startup, Sense, a pioneering enterprise data science platform, was acquired by Cloudera in 2016. Continual’s cofounder and CTO, Tyler Kohn, built RichRelevance, the world’s leading personalization provider, before it was acquired by Manthan in 2019. Tristan and Tyler saw the huge gap between the transformational potential of AI and the day-to-day struggle most companies faced operationalizing AI using real world data. They founded Continual to radically simplify operational AI by taking a fundamentally new approach.

“Artificial intelligence has the potential to transform every industry, department, product and service but current solutions require complex infrastructure, advanced skills, and constant maintenance. Continual breaks through this complexity with a radical simplification of the machine learning development lifecycle, combining a declarative approach to operational AI, end-to-end automation, and the agility of the modern data stack. Our customers are deploying state-of-art predictive models that never stop learning from their data in minutes rather than months,” said Tristan Zajonc, CEO and cofounder of Continual.

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“Getting continually improving predictive insights from data is critical for businesses to operate efficiently and better serve their customers. Yet operationalizing AI remains a challenge for all but the most sophisticated companies,” said David Beyer, Partner at Amplify Partners. “Continual meets data teams where they work – inside the cloud data warehouse – and lets them build and deploy continually improving predictive models in a fraction of the time existing approaches demand. We invested because we believe their approach is fundamentally new and, most importantly, the right one to make AI work across the enterprise.”

With the new capital, Continual plans to more than double its team over the next year with new hires for sales and engineering roles. It will expand into new AI/ML use cases such as NLP, realtime, and personalization, and broaden support for additional cloud data platforms. Continual is offering a 14-day trial with its open beta release, enhancements for dbt users, and support for Snowflake, Redshift, BigQuery, and Databricks.

“dbt was built on the idea that the unlock for data teams is a collaborative workflow that brings more people into the knowledge creation process. Continual brings this same viewpoint to machine learning, adding new capabilities to the analytics engineers’ tool belt,” said Nikhil Kothari, Head of Technology Partnerships at dbt Labs. “We’re excited to partner with Continual to help bring operational AI to the dbt community.”

“Continual is enabling organizations to easily build, deploy, and maintain continually improving predictive models directly on top of Snowflake,” said Tarik Dwiek, Head of Technology Alliances at Snowflake. “As part of our partnership, we’re excited to help bring these benefits to the Snowflake community and to accelerate end-to-end machine learning workflows on top of Snowflake with Snowpark.”

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