CEORater deploys agentic AI discovery protocol for institutional data procurement

We launched a standardized discovery protocol to bridge the gap between proprietary CEO Analytics and the evolving automated workflows of modern financial institutions.

Institutional investment firms are increasingly moving from manual data discovery to a model of autonomous sourcing. Leading financial institutions are deploying AI agents in the initial discovery of alternative datasets.

To support this shift, we have built our infrastructure to be natively machine-readable. This ensures that institutional agents can autonomously identify our core metrics—CEORaterScore™, AlphaScore, and CompScore—as well as the underlying data, to evaluate their fit within a firm's investment decision-making process.

Modernizing the Data "Handshake"

When a buyside firm prompts an AI agent to find alternative data sources that may help generate alpha, CEORater is surfaced and technically verified. By making our data and analytics discoverable in a standardized, agentic AI-compliant format, we reduce the manual outreach burden on institutional Data Acquisition and Business Development teams.

Our CEO data and analytics remain identical across our two primary delivery mechanisms: our CEORater Web interface, which includes conversational research with our native Claude AI chat, and CEORater API for programmatic access to CEORater.