We’re going to share a project that’s a much larger scale than our previous entries this week to introduce Civic’s Revere, a new CRM going through the approval process with the House. Civic co-founder Jon Kokot recently explained to me that much of the product is designed around the pain points of current systems, which he heard about from his contacts serving as a naval congressional liaison. The major culprit in current CRMs was staff having to manually extract data from a tidal wave of inbound information to make it usable. He drew from examples of the recent explosion of AI-enabled fintech products as a comparative point for the congressional market, where the automation of data processing from a range of sources can free staff to focus on higher-level tasks, which in the congressional case is legislative efforts and casework. Jon gave a demonstration of Revere at last year’s Congressional Hackathon.
Revere operates closed AI models running locally to streamline processing constituent correspondence and casework management in a secure environment. Because it’s built on fresh distributed architecture, it can run multiple high-volume tasks at once without affecting performance. Models take inbound communications, apply office tags, batch them topically, and compile the constituent record. They also move casework out of a ticketing system, adding data and documents as a case heads through an agency’s process for centralized management. Kokot believes the system can cut down on casework delays ultimately if both congressional and agency staff interact through Revere.
The platform also allows member offices to run queries against the data in their CRM to support the trendspotting and review they routinely perform much more efficiently. Looking across the data, an AI knowledge manager assistant can perform sentiment analysis on issues constituents wrote in on, identify emerging policy concerns, and identify agency sticking points.
Modern communications technology has been a paradox for government, creating new inefficiencies in workflows when efficient communications methods create greater and greater scale. We’ve been hopeful that new innovations could break this paradox for some time, usually by layering on a specific fix to legacy systems. Civic is making more of a fresh start, which is long overdue and now much more feasible through AI.
