๐ช๐ต๐ฎ๐ ๐ฌ๐ผ๐ ๐ช๐ถ๐น๐น ๐๐ผ
โข ๐๐ป๐ป๐ผ๐๐ฎ๐๐ฒ ๐ฝ๐ผ๐น๐ถ๐ฐ๐ ๐๐ผ๐๐ฒ๐ฟ๐. Locate and annotate the limits structure within a given excess policy โ limits, sublimits, retentions, attachment points, and how the layers stack.
โข ๐ฅ๐ฒ๐ป๐ฑ๐ฒ๐ฟ ๐ฐ๐ผ๐๐ฒ๐ฟ๐ฎ๐ด๐ฒ ๐ฐ๐ผ๐ป๐ฐ๐น๐๐๐ถ๐ผ๐ป๐. Given an insuring agreement and its ensuing exclusions, produce a structured conclusion about how coverage responds to a described scenario.
โข ๐ค๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐ฟ๐ฟ๐ฒ๐ฐ๐ ๐น๐ฎ๐ฏ๐ฒ๐น๐ถ๐ป๐ด. Review AI-generated output, fix mislabeled data, and grade agent performance against what a seasoned practitioner would conclude.
โข ๐๐ฑ๐ท๐๐ฑ๐ถ๐ฐ๐ฎ๐๐ฒ ๐๐ต๐ฒ ๐ต๐ฎ๐ฟ๐ฑ ๐ฐ๐ฎ๐๐ฒ๐. Resolve the ambiguous, conflicting, and unusual situations โ manuscript wordings, follow-form excess, nested exclusions, schedule-of-underlying disputes, implicit market conventions โ that determine whether a product is trusted by real practitioners.
โข ๐๐๐ถ๐น๐ฑ ๐ฒ๐๐ฎ๐น๐๐ฎ๐๐ถ๐ผ๐ป ๐ฟ๐๐ฏ๐ฟ๐ถ๐ฐ๐. Partner with our team to design the scoring criteria and review workflows that tell us, line by line, whether an extraction or a coverage conclusion is accurate.
โข ๐ฆ๐ต๐ฎ๐ฝ๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐ ๐ฏ๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ. Help us decide how coverage should be interpreted and presented โ what a practitioner needs to see, in what structure, and what would mislead them. Translate practice into product requirements.
โข ๐๐ฒ ๐ผ๐๐ฟ ๐๐ฟ๐ฎ๐ป๐๐น๐ฎ๐๐ผ๐ฟ. Sit between the documents and the engineers: explain terminology, market convention, and the โwhyโ behind how things are done, and flag where our assumptions do not match reality across lines of business.
โข ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐ด๐ฟ๐ผ๐๐ป๐ฑ ๐๐ฟ๐๐๐ต. Review real policies, endorsements, and claims documents and tell us the correct answer โ coverages, limits, sublimits, retentions, attachment points, exclusion logic, and how layers stack โ so we can measure and improve the AI against an expert standard.