Claims and document volume in insurance is never flat — it spikes with renewal season and explodes after a catastrophe. We engineer high-volume model serving that autoscales to the surge, holds cost per token down at steady-state volume, and works the backlog in priority order instead of dropping it.
Insurance inference runs at two extremes at once. Day to day it is high, steady volume — claims intake, document extraction, policy review — where cost per token compounds across millions of items and a few cents the wrong way becomes a budget problem. Then a catastrophe hits, or renewal season lands, and that volume multiplies overnight. A serving layer provisioned for the average drowns in the spike; one provisioned for the peak burns money the other fifty weeks of the year.
The answer is serving that follows the curve. We build capacity that autoscales to the surge and stands back down when it passes, with queueing and backpressure so a flood of claims is absorbed and worked down by priority rather than dropped. At steady state we keep cost per token tight, and the whole stack can run inside your environment so policyholder data never leaves.
A serving layer engineered to ride the claims curve — steady volume at low cost, and surge capacity when the curve breaks.
Value concentrates wherever document volume is high, spiky, or both — and where the per-item cost has to stay defensible:
Insurance volume is anything but flat — a catastrophe event or a renewal season can multiply claims and document intake overnight. We build serving that scales to that curve with autoscaling tied to queue depth, plus queueing and backpressure so a surge is absorbed and worked down in priority order rather than dropped. Capacity scales back when the event passes, so you carry headroom for the spike without paying for it the rest of the year.
Carriers run enormous, steady document and claims volume, so cost per token compounds fast. We hold it down with high GPU utilization from continuous batching, INT8/INT4 quantization validated against your accuracy bar, and right-sized model routing so a simple document extraction never invokes an expensive model. Cost per token is tracked on every request and can be attributed to a line of business, and the whole stack can run on-prem so policyholder data stays inside your environment.
Bring your steady-state claims volume and the surge a catastrophe or renewal season throws at it. In thirty minutes we will show how autoscaling serving rides the curve — and how we will hold cost per token down. 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: