Claims and risk modeling, plus document and vision processing at volume, are the workloads that drive insurance AI compute. We size an owned baseline for everyday load and provision elastic burst for seasonal and catastrophe-driven surges, so capacity scales with the event and the bill comes back down after.
Insurance compute is driven by events. A catastrophe or a renewal cycle can multiply claims and document volume in a matter of days, and the AI workloads underneath — catastrophe and risk modeling, document extraction across policies and claims forms, vision-based damage assessment from claim photos — all scale with it. Provision for the worst case year-round and you pay for a CAT-event fleet during every quiet month; provision for the average and the surge buries the operation when it matters most.
The answer is capacity planning that respects the shape of the load. We size an owned baseline for everyday claims and underwriting, then provision elastic burst that can absorb a surge on demand — modeled against your historical seasonality and catastrophe patterns rather than guessed. Each workload has its own profile, so we carry the steady mix on the baseline and schedule the bursty pieces into it instead of buying separate hardware for modeling, documents, and vision.
GPU capacity sized for the everyday mix and planned to absorb the seasonal and catastrophe-driven spikes.
Value concentrates wherever volume is high, the load is event-driven, and capacity has to flex without overbuying:
Insurance load is seasonal and event-driven — a catastrophe or a renewal cycle can multiply claims and document volume in days. We size an owned baseline for everyday claims and underwriting, then provision elastic burst that can absorb a surge without provisioning for the worst case year-round. The capacity plan is modeled against your historical seasonality and CAT events, so when volume spikes the compute scales and when it subsides the bill comes back down.
Three drive most of the demand. Risk and catastrophe modeling runs large simulation and scoring jobs that GPUs accelerate dramatically. Document processing at volume — policies, claims forms, medical records, and correspondence — is heavy extraction and classification work. And vision workloads like damage assessment from claim photos are pure GPU inference. Each has a different load shape, so we size the fleet to carry the steady mix and schedule the bursty pieces into it rather than buying separate hardware for each.
Bring your claims, modeling, and document volumes and how they move with seasons and events. In thirty minutes we will show a baseline-plus-burst capacity plan that absorbs the spike and recedes after. 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: