Data copilot startup Prophecy Inc. said today it has closed on a $47 million Series B extension round, as it bids to transform how companies access and manipulate their corporate data with generative artificial intelligence.

Today’s round was led by Smith Point Capital, and saw participation from new investor HSBC and existing backers such as Berkeley SkyDeck, DallasVC, Insight Partners, JPMorgan Chase and SignalFire.

The startup is the creator of what is believed to be the first copilot for Databricks, using generative AI to simplify and streamline the preparation of raw business data for AI applications and analytics workloads.

Prophecy’s copilot makes corporate data, both structured and unstructured, easily accessible to users no matter where it’s located, be it in the cloud or in on-premises servers. It does this with an AI-powered visual designer tool that quickly generates standardized and open code to extract, transform and deliver information –a process known as ETL — from where it resides to where it needs to be. In other words, it automates the creation of the required data pipelines and tests needed to move data around different computing systems.

It also generates documentation, and can suggest fixes to any coding errors on existing data pipelines, so as to modernize and consolidate them.

In an interview last year on SiliconANGLE Media’s mobile livestreaming studio theCUBE, Prophecy founder and Chief Executive Raj Bains (pictured) explained that his company provides the tooling layer that makes data engineers, data analysts and business users more productive.

“A data analyst using a visual drag and drop and some generative AI can just say: ‘Build me a pipeline that does this,’ and we build it for them,” Bains explained. “That runs on Databricks native code at scale.”

The startup says business is thriving because enterprises everywhere are scrambling to accelerate their AI initiatives. AI applications require access to vast amounts of data, and so Prophecy’s copilot is emerging as a key enabler of this trend. In the last full year, Prophecy saw its revenue grow more than 3.5 times, with a 160% net revenue retention rate from its existing customer base.

Its competitors include notable technology firms such as Alteryx Inc. and Informatica Inc. Customers include the Major League Baseball team Texas Rangers, which leverages the Databricks platform and Prophecy to build pipelines for hundreds of millions of legacy data records. They’re eagerly embracing its copilot, because the engineers required to retrieve and integrate data from scattered systems are increasingly scarce, and the task of manually building data pipelines can take many months. With Prophecy claiming it can do the job in a matter of minutes, it’s not difficult to see the appeal.

In addition, Prophecy also helps to streamline the integration of modern data environments with legacy ETL and data engineering tools, which were built for traditional data warehouse and on-premises deployments rather than today’s cloud-native data environments.

“Legacy data engineering tools do not work well in modern data environments,” International Data Corp. said in a 2024 report. “Modern data engineers need code-free and code-friendly technologies because data integration and transformation are now occurring at all organizational levels, including in IT and the line of business.”

Bains said that some of his customers face 12-month backlogs in their data integration efforts, delaying the deployment of urgent AI projects.

“Prophecy’s holistic approach combines visual design, code and AI to serve data engineers and analysts on a common platform for maximum teamwork and productivity,” he said. “This new investment will accelerate our roadmap and is already helping us scale to serve more customers in 2025.”

Smith Point Capital co-founder and Managing Director Burke Norton said Prophecy is fundamentally changing the way enterprises think about data transformation.

“By democratizing data proficiency and access, Prophecy is solving a multibillion-dollar productivity challenge for enterprise data teams who typically spend 80% of their time preparing data for analytics and AI, rather than implementing it,” he said.

Here’s Bains’ full interview, where he talks more about the potential of generative AI in data transformation:

Photo: SiliconANGLE