FactSet partners with Google Cloud to enhance financial intelligence with AI
The financial data giant deepens its Google Cloud relationship as part of a broader push to embed generative AI across institutional finance workflows.
FactSet is expanding its partnership with Google Cloud to develop advanced AI tools for financial intelligence, building on a relationship that traces back to at least April 2022. The collaboration aims to combine FactSet’s institutional-grade financial datasets with Google Cloud’s AI and machine learning infrastructure.
FactSet first made its proprietary datasets available through Google Cloud Analytics Hub back in April 2022, giving users the ability to tap into Google Cloud’s analytics and machine learning tools alongside FactSet’s financial data.
FactSet’s broader AI offensive
In March 2026, FactSet deployed an AI beta to over 85,000 users, introducing AI-enabled document search features that let analysts query financial documents using natural language rather than keyword-based searches.
Then in May, FactSet unveiled its “FactSet Intelligence” platform at the company’s FOCUS 2026 event. The platform is built around connected data workflows and what the company describes as agentic AI, meaning AI systems that can take multi-step actions on behalf of users rather than simply responding to prompts.
Beyond its own product development, FactSet has established collaboration agreements with OpenAI and Anthropic. The company serves as a launch partner for OpenAI’s finance tools, enabling direct integration between ChatGPT and FactSet’s data. That means when a ChatGPT user asks a finance question, the answer can be grounded in FactSet’s verified datasets rather than whatever the model scraped from the internet during training.
FactSet also runs an AI Partner Program that licenses institutional-grade financial data to AI developers more broadly. The goal is to ensure that as third-party AI applications proliferate across finance, they’re working with regulated, high-quality data rather than the kind of noisy, unstructured information that leads to costly errors.