Claims and underwriting run on policyholder data you are obligated to protect — at a volume that makes a metered API expensive. We deploy self-hosted open models — Llama, Mistral, Qwen — in your environment so policyholder PII stays in-house, fine-tuned on policy and claims language, with the unit economics engineered to control cost across high-volume automation.
Insurance work is dense with regulated personal data — policyholder PII, claims narratives, medical records, financial detail — and a metered LLM API requires sending that material outside the carrier to a vendor's servers. Across many states, lines, and privacy regimes, that crossing is a liability you do not need to take on. Open-weight models keep it in-house: the model runs in your environment, inference happens where the data lives, and policyholder information never reaches a third-party endpoint.
The economics reinforce the choice. Claims and service operations are high-volume by design — every first notice of loss, document, and correspondence is a token cost — and on a metered API that bill scales with the very automation you are trying to make cheaper. A self-hosted open model fixes cost to capacity you own, lets you tune on the language of your policies and claims, and removes dependence on a single vendor's pricing. We help you find where that line pays off and build the system to run on it.
Open models selected, adapted, and served around policyholder data, claims volume, and insurance language.
Value concentrates wherever policyholder data must stay in-house, language is insurance-specific, or claims volume is high:
Yes. We deploy Llama, Mistral, or Qwen inside your environment so policyholder PII, claims files, and medical and financial detail are processed where they live and never sent to a third-party API. For a carrier handling regulated personal data across many states and lines, that keeps the data inside your control and inside the regulatory boundary you already answer to.
Yes — claims is exactly the volume profile where it pays off. Every FNOL summarized, document classified, and letter drafted is a token cost, and on a metered API that line grows with every claim. A self-hosted open model fixes cost to capacity you own; we model your throughput against current spend and serve with quantization and batching so per-claim cost stays low and predictable as automation scales.
Bring your highest-volume claims or underwriting task and the policyholder data it runs on. In thirty minutes we will show how a self-hosted open model performs against your current API — on quality, on per-claim cost, and on data control — and how we would take it to production. Response inside 24 hours.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: