AI document analysis at scale in life insurance

Starting point

An insurer processes a five-figure volume of document-intensive cases annually in its life insurance operations. Each case involves a bundle of medical records — typically 20 to 50 pages. Manual processing required 15 to 45 minutes of examiner time per case. At industry-standard fully loaded costs, this represented a substantial cost base. The question was not whether AI-assisted analysis would be economically viable — but whether the platform could be operated under the regulatory conditions of a supervised insurer.

What we did

We built an AI platform for structured analysis of medical document bundles and scaled it for the insurer's specific volume. The platform classifies documents automatically and extracts domain-relevant data into a structured format. As part of the pilot, we produced a complete cost analysis: infrastructure costs, AI inference costs across multiple model generations, and overhead factors for production use. The result: running platform costs below one euro per case. In addition: hosting recommendation, compliance documentation, and security architecture for regulated operations. Team: 3 people over 8 weeks.

Results

What we learned

The cost structure of AI-powered document processing has fundamentally changed in 18 months. Where six-figure annual costs were recently required for comparable quality, model costs are now 25–50x lower. Infrastructure is no longer the bottleneck — the question is whether the organisation is ready to change the process.