The venture studio of a major North American pension plan (hundreds of billions in assets under management) was evaluating an insurance-focused venture whose viability hinged on one technical question: can AI reliably extract structured data from the industry's messiest documents?
Statement-of-value files, sprawling Excel sheets, and OCR-heavy PDFs had to be parsed and understood before the studio could justify committing capital to a full build. The studio rarely outsources development, making this a targeted feasibility bet with a hard decision point at the end.
Lazer built a six-week feasibility pilot that tested AI models, processes, and libraries against real insurance documents. Anthropic's Claude was selected as the parsing engine within a swappable-model framework, running on a Python and Temporal pipeline on AWS, with Weights & Biases evaluations validating accuracy throughout.
The pipeline hit 80% accuracy on the OCR-heavy documents where table-transformer approaches had failed completely, the number that made the venture decision concrete.
Three weeks of disciplined AI R&D plus three weeks of engineering produced a working pipeline the studio could demo to prospective customers.
The studio got its feasibility answer with evidence, plus a model-swappable framework that would not need rebuilding as the AI landscape shifted.
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