OpenAI is taking aim at some of Wall Street's most labor-intensive tasks with a new version of ChatGPT designed to research companies, analyze financial data and generate the presentations that investment bankers rely on.

The product unveiled Thursday, called ChatGPT for Financial Services, is a tailored version of its enterprise product, ChatGPT Work, that was made with "design partners" Morgan Stanley and Evercore , according to OpenAI's Vice President of Product, Nick Turley. It uses the AI company's latest and most advanced model, GPT-6 Astra.

The rollout puts OpenAI deeper into territory traditionally occupied by Wall Street's entry-level bankers, the recent college graduates called analysts and associates that the industry has employed for decades to research deals and create pitchbooks. It also showcases the company's continued push into enterprise offerings as it gears up for what is widely expected to be a blockbuster IPO.

"We're effectively teaching ChatGPT to research like an analyst and back up its conclusions like an analyst as well," Turley said during a briefing announcing the new product.

OpenAI has spent much of the last year racing to win over business customers in the fiercely competitive enterprise market, where it's working to fend off rivals including Anthropic and Google . Anthropic announced its own tailored solution for Wall Street, Claude for Financial Services, last year.

Sarah Friar, OpenAI's finance chief, told investors in August that the company's enterprise business accounted for more revenue than its consumer business, which took off following the launch of ChatGPT in 2022.

Turley told reporters during the briefing OpenAI plans to release tailored solutions for "a number of sectors" beyond financial services.

In a live demonstration of the new offering, Turley showed the platform analyzing a potential M&A target, pulling financial figures from industry-standard data sources and creating a formatted PowerPoint deck based on a bank's preformatted style guide.

"It's very easy to make slides that look good, but it's much harder to make slides [that] actually make sense," Turley said. "To get here, ChatGPT had to choose the relevant peers. It had to pull the prices into a spreadsheet. It had to check the chart against the data, and it had to explain the selloff and the rebound."