Klarent Launches Unified AI-Powered Policy Administration System for Personal-Auto MGAs

Klarent has launched a unified policy administration system (PAS) specifically designed for personal-auto managing general agents (MGAs). By integrating underwriting, billing, and reporting into a single data environment with embedded AI, the platform aims to eliminate the data silos that typically delay decision-making and product adjustments in the competitive auto insurance sector.

What was announced

The new platform is a comprehensive suite covering the entire insurance lifecycle, from customer acquisition and agent quoting to binding, servicing, and claims reporting. Its primary differentiator is the use of a shared set of data and definitions across all functions. This ensures that metrics remain consistent whether they are viewed on an underwriting screen or a management report, removing the need for manual reconciliation between disparate systems.

The system includes embedded AI-assisted analytics and workflow automation designed to streamline day-to-day operations. For instance, the underwriting AI monitors post-binding activities, such as flagging missing documentation like proof of prior insurance. It can automatically initiate contact with policyholders via call or text to collect required files and then summarize the contents for human underwriters to review. This automation allows underwriters to focus on the final decision rather than the administrative task of document collection and initial verification.

Additionally, Klarent offers optional telematics capabilities. This allows MGA teams to view driving-behavior data alongside traditional policy and claims information within the same interface. By treating telematics as an integrated data stream rather than a separate silo, MGAs can make more informed underwriting and product decisions. The platform is being showcased at ITC Vegas 2026, held from September 29 to October 1 at the Mandalay Bay Convention Center in Las Vegas, where the company is demonstrating the platform at booth 2054.

"When the same business question produces different answers from different systems, teams spend their time reconciling reports before they can make a decision. We built Klarent, so its operating tools and reports use the same definitions."

Kumar Maddali, VP Engineering & AI at Klarent.

The companies involved

Klarent is a technology provider focused on the insurance sector, operating as a Telenav company. Telenav is a well-established player in the connected car and location-based services market, which provides Klarent with a significant foundation in automotive data and telematics integration. This background is particularly relevant given the platform's focus on personal-auto MGAs, a segment of the market that increasingly relies on real-time driving data to refine risk assessment and pricing models.

Novo Insurance, an early adopter of the Klarent platform, is an affiliate of Telenav. As an MGA, Novo Insurance utilizes the system to accelerate the speed at which it can bring approved product changes to market. The relationship between Klarent, Novo, and Telenav suggests a vertically integrated approach to developing insurance technology, where software development is closely aligned with the practical operational needs of an active insurance provider. This positioning allows Klarent to compete in the crowded policy administration system market by offering specialized tools for auto-specific risk management. By leveraging Telenav’s expertise in automotive technology, Klarent enters the market with a specific focus on the intersection of vehicle data and insurance operations.

What this means

The launch of Klarent’s PAS highlights a growing industry shift toward "single source of truth" architectures in the MGA space. As personal-auto margins tighten due to inflationary pressures and changing driver behavior, the ability to act on underwriting performance data in real-time becomes a critical competitive advantage. Legacy systems that separate operational data from analytical insights are under increasing pressure from unified platforms that reduce the "decision-to-action" lag. By embedding AI directly into the workflow rather than treating it as a post-facto reporting tool, Klarent is addressing a specific bottleneck in document verification and risk selection. The success of such platforms will likely depend on how effectively they can ingest and normalize diverse telematics data from various vehicle manufacturers.