Protecting capital in today's markets requires immense speed and precision. A financial analyst preparing a deal memo works across licensed market data, internal models, and confidential client files. General-purpose AI lacks the real-time accuracy, verifiable data lineage, and strict security that financial institutions demand. While model intelligence is necessary, without deep integration into trusted financial systems, it is not sufficient.
Making AI genuinely useful inside an industry requires four things, together: domain expertise encoded into reusable skills, secure connections to the systems and data the work depends on, agents that can act inside real workflows, and an open ecosystem that extends and scales all of it — with governance running underneath all four. Only together do they produce something an institution can actually put into production and see true return on investment.
Today, we are delivering on this vision with Gemini Enterprise for Financial Services , bringing Google’s agentic AI directly into the workflows of capital markets and corporate banking. Bringing Gemini Enterprise into the workflows of capital markets and corporate banking Gemini Enterprise for Financial Services delivers an integrated, secure environment configured for rapid deployment with four core components: 1. Purpose-built financial skills.
Skills are reusable packages of instructions and context that teach an agent to run a specialized task the way your institution runs it — applying custom formatting to a report, pulling a specific data cut, following a defined research methodology. They are available inside the Financial Research agent and to any agent your teams build. Secure Model Context Protocol (MCP) connectors. Direct integrations, using MCP, into essential financial platforms and licensed data sources, configured inside your own environment.