Striveworks Inc., the developer of a machine learning operations platform that makes it easier to build and deploy artificial intelligence models, has raised $33 million in fresh funding.

The startup announced the investment this morning. Growth equity firm Centana Growth Partners was the lead investor, while a number of existing backers contributed as well.

Austin-based Striveworks offers a platform called Chariot that reduces the amount of time necessary to deploy custom AI models. According to the startup, its platform allows software teams to complete tasks that usually take months in a few hours. One of Chariot’s flagship time-saving features is its no-code interface, which reduces the need for manual programming.

The first AI development task Chariot promises to speed up is the process of creating training datasets for supervised machine learning models. Supervised machine learning is a widely used approach to building neural networks. It’s also labor-intensive, which is why developers often automate parts of the task using software workflows. 

Supervised learning models learn by example. If a company is building an AI system to organize documents by topic, it must provide the system with a collection of sample documents that developers have organized in advance. Striveworks’ Chariot platform makes it easier to create such datasets.

Chariot allows users to aggregate raw training data, such as unorganized business documents, and add a descriptive label to each record. That label might, for example, specify the topic of the document to which it’s attached. During training, supervised learning models can analyze the dataset and learn how to organize business records. 

Striveworks’ platform also promises to ease other aspects of the AI development process. According to the startup, its platform enables software teams to organize all their models in one centralized catalog for easy access. Additionally, Striveworks provides a tool for verifying the reliability of the training datasets that are used to build new neural networks. 

After the AI development process is complete, developers can use Chariot to deploy their models in production. The platform eases the task of attaching an application programming interface, or API, to newly developed neural networks. An API in this case is a channel through which applications can send a neural network instructions and receive processing results.

Companies can run the models they build using Striveworks’ platform on multiple types of infrastructure. The startup says that its platform makes it possible to deploy models on, among other systems, connected devices located at the edge of the network. 

Striveworks’ annual recurring revenues tripled in the two years leading up to its newly announced funding round. The startup didn’t share absolute sales numbers. However, TechCrunch reported today that Striveworks has multiple seven-figure contracts as well as a high net retention rate, a metric used to track whether existing customers increase their spending over time.

“This new funding allows us to continue to build and refine our industry-leading MLOps platform to support our partners and enable them to efficiently manage the vast amount of data the world has to offer, bringing the platform to where the data and decisions are made,” said Striveworks Chief Executive Officer Jim Rebesco.

The startup will use the new capital to hire more salespeople, marketers and engineers. In parallel, it intends to build additional features for its platform. Striveworks’ growth plan places an emphasis on increasing its market presence in highly regulated industries. 

Image: Striveworks