December 16, 2021
Today we’re excited to announce the public beta launch of Continual, the first operational AI platform built specifically for modern data teams and the modern data stack. We’re also announcing our $4M Series Seed, led by Amplify Partners, and joined by Illuminate Ventures, Wayfinder, DCF, and Essence, as well as new partnerships with Snowflake and dbt Labs.
Continual is an operational AI platform for modern data teams and the modern data stack. It enables organizations of any size to deploy continually improving predictive models — from customer churn to inventory forecasts — directly on top of their cloud data warehouse. Powered by end-to-end automation and a declarative workflow, Continual unlocks data teams to deploy production-grade models in a fraction of the time without operational burden.
You can sign up directly as part of our public beta launch starting today. While we’re still early on our journey, we’d love to hear your feedback and work together to realize this vision.
Like many, we believe that machine learning and AI can transform every industry and business function. But after a decade of working on machine learning infrastructure at previous companies, we know this is not today’s reality. Today, deploying AI requires time-consuming manual effort for each use case, a team of highly skilled and generally time-constrained engineers and data scientists, and costly and complex machine learning infrastructure.
The result is an AI Gap — 95% of business leaders say their organization would benefit from embedding AI in their business, but only 6% report adoption of AI across their organization.1
This should be of no surprise. When every use case requires custom engineering, already overstretched data teams have no hope of keeping up. Even something as basic as maintaining a customer churn or inventory forecast can take months to make it to production. As a result, business leaders are frustrated by the lack of ROI from AI investments, and data teams are overwhelmed by managing the accumulating technical debt of ML pipeline jungles.
The path to AI has led us from the era of Big Data to the era of Big Complexity.
No-code AI tools don't solve the problem either. While promising increased accessibility and automation, these tools lack the sophistication and operational characteristics that modern data teams expect, such as version control, a separation between development and production environments, and workflow automation. Democratization and automation are the future, but operational robustness remains critical.
There must be a better way.
What if you could build continually improving predictive models directly on top of your existing cloud data warehouse? What if you could make operational AI accessible to your entire data team by extending their existing analytic skills and workflows? What if you could leverage a declarative approach to AI like SQL does for analytics?
That is what Continual seeks to deliver.
Continual is built natively for cloud data warehouses like Snowflake, BigQuery, Redshift, and Databricks. Data-driven companies everywhere are rapidly moving to these data platforms and embracing SQL as the lingua franca of data. The result is a unified foundation for a new modern data stack.
Continual sits directly on top of your cloud data warehouses and automates the maintenance of both the predictive models to ensure they never go stale and the model predictions so your business always runs on up-to-date insights.
By writing directly back to your data warehouse, you can easily consume up-to-date predictions from existing BI, Reverse ETL, and downstream tools. There is no new infrastructure or complex integrations to manage to make predictions actionable.
Continual is powered by a declarative workflow built to empower modern data teams, regardless of their ML expertise, to deploy production models across their business. At a high level, Continual operates in three steps:
While Continual does support UI-based development, it also has first-class support for dbt. dbt users can simply annotate existing data models to organize features into a shared feature store and automatically build and maintain new predictive models.
The result is a radically simplified approach to operational AI that allows you to focus on driving business impact from AI rather than infrastructure or operations. To understand how it all works, you can read more about Continual's core concepts and follow our quickstart.
Finally, Continual is operationally focused. It is designed for a world where you have dozens or even hundreds of models in production powering every aspect of your business from sales, marketing, operations, support, finance, and product. Building upon Continual declarative workflow, Continual maintains models and predictions with end-to-end automation, visibility, and governance baked in.
This focus on operational robustness, modern workflows, and complete visibility extends across the entire AI lifecycle from development to production. Using Continual, you can separate between development and production environments, leverage version control, and even integrate with CI/CD workflows all while avoiding pipeline jungles or complex engineering.
If you’re interested in learning more about how Continual can help you better understand your customers or operate more efficiently using machine learning and AI, we’d love to hear from you. You can:
We’re excited to talk with you and work together to deliver on this vision.
Today, I’m excited to share that I’ve joined Continual as Head of Marketing. Continual is radically simplifying the path to operational AI with the first continual AI platform built for the modern data stack.
Today, I’m extremely excited to introduce Continual. Continual is the easiest way to maintain predictions – from customer churn to inventory forecasts – directly in your cloud data warehouse. Read more.