Feathery Launches Coverage Compare: AI-Powered Proposal Tool for Insurance Brokers

Feathery has launched Coverage Compare, an AI-driven proposal tool designed to help P&C and employee benefits brokers automate the creation of client-facing documents. By reducing the manual burden of data entry from carrier quotes, the tool allows brokers to provide high-touch, data-rich proposals to smaller accounts that were previously too labor-intensive to service at scale.

What was announced

Coverage Compare targets the manual bottleneck in the insurance sales cycle. Brokers typically spend hours extracting data from carrier quotes, declaration pages, and endorsements to build custom proposals. Feathery’s tool automates this by allowing brokers to upload any carrier document, which the AI then organizes into a draft for review. The system integrates with AMS360 to pull existing client and policy details directly into the workflow.

The tool is designed to move beyond simple data extraction by providing industry-specific context. Key features include:

• Industry context: Recommendations and peer benchmarking tailored to the client's size and risk profile.
• Cyber risk analytics: Integration with CyberCube to provide specialized insights for cyber coverage.
• Side-by-side comparison: The ability to contrast expiring policies with renewal options within the draft.
• Robin AI assistant: An interface for editing drafts using plain language commands and pulling client details from the agency management system.

The Baldwin Group reported that building an initial benefit guide, which previously took 16 hours, now takes approximately 30 minutes using the platform. The tool is designed to use the agency’s own templates and approved disclosure wording, ensuring that the broker maintains control over the final output and branding. Every AI-generated change is tracked for broker approval to ensure compliance and accuracy.

The companies involved

Feathery positions itself as an AI operating and decisioning system for the insurance sector. Led by co-founder and CEO Peter Dun and co-founder Zack Khan, the company focuses on automating complex workflows across the insurance lifecycle, including submission intake and renewals. The firm has recently expanded its footprint in financial services, moving beyond basic data capture into sophisticated decision-support tools for intermediaries.

The Baldwin Group, which provided performance data for the launch, is a major national insurance brokerage and risk management firm. Their involvement highlights the tool's application within large-scale brokerage operations where efficiency in the middle-office is critical for maintaining margins on mid-market and small-business accounts. Feathery’s platform is built to sit between the carrier’s output and the broker’s client management systems, acting as a translation layer that converts raw policy data into actionable sales collateral.

What FF News has reported before

FF News has tracked Feathery’s rapid expansion into the AI-driven financial services space. In July 2026, the company secured $30M to scale its AI operating and decisioning system. This funding preceded several product launches, including AI portfolio submission analytics in June 2026 and a proposal generation tool for wealth management in August. Most recently, in September 2026, the company debuted Robin, the AI assistant that now powers the natural-language editing capabilities within Coverage Compare.

What this means from an insurtech perspective

This launch addresses a persistent friction point in the distribution value chain: the "proposal gap." For many MGAs and brokers, the cost of manual data entry often dictates the quality of advice given to smaller clients. By automating the extraction of data from disparate carrier documents, Feathery is putting pressure on incumbent agency management systems that have historically struggled with document intelligence. If brokers can commoditize the "heavy lifting" of proposal building, the competitive advantage shifts from operational scale to the quality of the underlying risk data and benchmarking. This move reinforces the trend of AI acting as a bridge between legacy core systems and modern client expectations.