Engineering deep-dives, research notes, compliance guides, and hard-won observations from building AI infrastructure for the most regulated corners of the insurance industry.
Retrieval-augmented generation works beautifully for general knowledge. It breaks in subtle, expensive ways when applied to insurance documents — cross-policy reasoning, dated endorsements, and conflicting clauses across a submission stack.
Retrieval-augmented generation works beautifully for general knowledge. It breaks in subtle, expensive ways when applied to insurance documents — cross-policy reasoning, dated endorsements, and conflicting clauses across a submission stack.
Deploying LLMs in regulated environments requires rethinking every assumption about logging, data residency, and model access patterns. Here's our architecture.
Auditors are asking about AI now. Most compliance frameworks weren't written with generative models in mind. We mapped every output to existing controls and found the gaps.
We shipped fast, broke things, and learned what matters. Here's an honest retrospective on the UX and technical decisions that aged well — and those that didn't.
Without a formal standard, the industry is converging on de-facto patterns for model governance, data handling, and explainability. Here's what we're seeing across the sector.
General-purpose embedding models degrade on niche terminology. We tracked cosine drift across six months of real policy language and share our mitigation playbook.
