Performance optimization
Performance
Continuous performance optimization
Cytora combines multiple models enabling insurers to maximize process automation and provides control through integrated training, quality control and operational workflows. Models compete to provide field candidates for target schema fields. Cytora captures training data independently of models enabling insurers to easily reuse training data across different model types and easily operationalise new models as they become available. Model agnostic schemas, quality control and performance monitoring mean that models can be continuously monitored, compared, quality assured, and substituted based on their operational performance. Fully integrated feedback loops enables performance optimization enabling the platform to learn continuously based on operational usage.
Latest advances
Latest advances in Generative AI
Models are integrated into reusable digitization flows when they become available which means insurers have access to the latest model advancements in Large Language Models and Generative AI. Unified risk schemas can be fulfilled by multiple models enabling globally optimized performance across different regions and transactional flows.
Control
Unified operational workflows
Model training, approval, exception management and operational workflows operate globally across all models so models can be compared, combined and substituted as technology landscape advances. At the same time Cytora insulates digitization workflows from model proliferation. As new large language models become available, insurers using Cytora can reuse training data, target schemas, and quality control processes, and operational workflows.
Feedback loop
Performance maximisation across workflows
Digitization of the risk intake is underpinned by a unified data model which is used to ensure the origin of all field values is recorded in full granularity. This schema is used by Cytora’s quality control interface to write back updates to field values that have been provided or changed by human operators. As far as the platform’s data model is concerned, human operators are considered as another source type, alongside LLMs and other automated components. This makes it possible to evaluate digitization performance when compared to human authored values (as found in downstream systems) and provides live training examples for continuous model enhancement.
“
We pick better risks and with the speed, save operating expenses. Cytora means we can come to better underwriting decisions, save operating expenses, so we increase our margins."
President Starr Insurance Holdings
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