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RecordLinker

ML-powered record linkage and data normalization for insurance AMS migrations

Data & Analytics Startup Bootstrapped
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Overview

RecordLinker is a data normalization and entity resolution platform for the property and casualty insurance industry, serving independent agents, brokers, and carriers that need to standardize records across Agency Management Systems (AMS). The platform addresses the challenge of connecting data elements across systems that use different names or codes for the same concept -- for example, a line of business labeled differently in each AMS.

The product uses machine learning algorithms to automatically identify and match records across disparate data sources. During AMS migrations and post-acquisition integrations, the platform auto-maps up to 90% of record pairs, leaving users to review and confirm the remainder rather than performing fully manual mapping. The company was founded after its team worked through normalizing 150 million insurance policy records and built a reusable toolset from that experience.

RecordLinker is bootstrapped with 1-10 employees and participates in the Guidewire Insurtech Vanguard Program. The platform supports migrations involving all major AMS platforms used in the P&C market.

Products & Services

AMS Migration

Supports data migrations between all major agency management systems, including QQ Catalyst, TAM, Sagitta, EZLynx, HawkSoft, Vision, Gen4, ConceptOne, NowCerts, Momentum AMS, Broker Buddha, Applied Epic, and AMS360. The platform is vendor-agnostic and can handle conversions between any source and target AMS combination.

Key Features

  • Pre-mapping capability available 8 weeks before migration start date
  • Compatible with all major P&C AMS platforms
  • No full database restoration required to begin mapping

Target Users: Independent agencies and brokerages undertaking AMS platform transitions

Entity Resolution

Machine learning-based record matching that automatically identifies which records across two systems refer to the same carrier, product, policy type, or other entity.

Key Features

  • Auto-mapping rates up to 90% of record pairs
  • Handles discrepancies in naming conventions, codes, and identifiers
  • Reduces manual mapping to a review-and-confirm workflow

Target Users: Data migration teams and conversion administrators

Reference Data Management

Normalizes carrier names, product lines, and policy reference data across systems to create a consistent data foundation for analytics and reporting.

Key Features

  • Standardizes carrier and product identifiers across AMS sources
  • Supports post-acquisition data consolidation
  • Creates a clean data layer for downstream reporting

Target Users: Insurance brokers and carriers managing multi-system data environments