Jev creator TypeSafe closes $870M round at $7.5B valuation

TypeSafe Inc., the creator of the Jev artificial intelligence model, today announced that it has raised $870 million in funding at a $7.5 billion valuation.

Andreessen Horowitz led the round with contributions from Sequoia Capital, DCVC and unnamed angel investors. The cash infusion comes less than a month after TypeSafe launched Jev. The company’s funding momentum reflects the surging popularity of its model: it has already been adopted by about a third of the Fortune 500.

When an enterprise application needs to perform a task using a large language model, it automatically sends a description of the task to the model. The LLM then responds with its output. That output often takes the form of natural language text. Applications must condense the natural language text into a structured, standardized format before they can put it to use.

Jev partly owes its popularity to the fact that it skips the latter step. When an application asks Jev to perform a task, the model generates structured output instead of natural language text. That removes the need for applications to reformat and condense Jev’s output before using it.

Jev supports just three types of requests. Applications can ask the model to answer a question with the equivalent of “yes” or “no”, pick an item from a list or generate a score. Developers can customize what that score measures. For example, Jev can be configured to rate cybersecurity alerts based on their severity or quantify the urgency of a support ticket.

The fact that the model’s terse output is easy for applications to process reduces the amount of data preparation code that developers must write. As a result, software projects can be completed faster. Furthermore, simplifying an application’s code base reduces the risk of errors.

Jev also boosts application reliability in other ways. When the model generates a score or chooses from a list of user-provided options, it outputs a number that indicates its confidence in the accuracy of the response. Applications can use that number to mitigate the impact of hallucinations.

TypeSafe says that it developed Jev using a new training approach called reinforcement learning for calibrated decisions. It’s a variation of reinforcement learning, a popular LLM training method. TypeSafe also developed a new model architecture.

The company claims that its custom technologies enable Jev to process user requests in under 700 milliseconds. That makes the model up to 200 times faster than some frontier LLMs. Furthermore, TypeSafe says that it’s up to 100 times more cost-efficient.

Jev is part of a planned model series dubbed System One. TypeSafe will use the proceeds from its funding round to make more additions to the lineup. Additionally, the company plans to roll out unspecified “enterprise features” that will make it easier for large organizations to use its models.

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