Document review and discovery are bursty, batch-heavy workloads — a matter lands, hammers the hardware, then goes quiet. We right-size a modest owned baseline with elastic burst for peak matters, run review at high utilization, and report AI compute as a cost per matter you can attribute.
Legal compute does not arrive in a steady stream. A large matter or discovery production lands, drives the GPUs flat out for days or weeks while documents are classified, reviewed, and privilege-checked, then the load falls away until the next one. Sizing owned hardware to cover that peak guarantees most of it sits idle between matters — the single most expensive way to run a spiky workload, and the failure mode legal teams fall into most often.
The right shape here is mostly elastic. We size a modest owned baseline for the everyday review that runs all the time, then lean on cloud or reserved burst for the peaks, so you pay for the spike only while it lasts. Because review is throughput-bound batch work with no live latency SLA, we can batch aggressively, pack the hardware to high utilization, and use spot and off-peak capacity — pushing the cost per document, and the cost per matter, down to a number you can attribute and pass through.
GPU capacity sized for spikes, run at high utilization, and costed per matter rather than buried in overhead.
Value concentrates wherever bursty, document-heavy workloads meet a need to control and attribute cost:
Usually not entirely. Legal compute is spiky by nature — a large matter or discovery production lands, hammers the hardware for days or weeks, then goes quiet. Owning enough GPU to cover the peak means most of that hardware sits idle between matters, which is exactly the case where cloud or reserved burst wins. We typically size a modest owned baseline for steady, everyday review and lean on elastic burst capacity for peak matters, so you pay for the spike only while it lasts.
Document review is throughput-bound batch work, which is the easiest kind of load to run cheaply. Because there is no live latency SLA, we batch aggressively, pack the GPUs to high utilization, and run on spot and off-peak capacity where the schedule allows — driving the cost per document, and ultimately the cost per matter, down hard. We also report compute as a per-matter line so it can be attributed and, where appropriate, passed through, rather than disappearing into general overhead.
Bring your review and discovery volumes and how they spike across matters. In thirty minutes we will show a baseline-plus-burst GPU shape and a cost-per-matter model that keeps compute attributable. 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: