A document review that has to clear millions of files before a production deadline is a throughput problem, not a chatbot. We engineer high-throughput model serving that finishes large batches inside the window, holds cost per matter predictable, and absorbs the bursty load a litigation timeline throws at it.
Legal inference is dominated by volume. Discovery review, contract analysis, and due diligence push millions of documents through a model against a fixed deadline, so the number that matters is how many the cluster clears per hour — its sustained throughput — not how quickly any single answer comes back. A serving layer tuned only for interactive latency leaves most of that throughput on the table and blows the production window.
The second constraint is the way the work is paid for. Legal is billed and budgeted by the matter, so an inference bill that cannot be tied to a matter is a problem. And the load is bursty: a new filing or a closing can spin up an enormous review with little notice, then go quiet. We engineer serving for sustained throughput, attribute cost cleanly to the matter, and scale to the burst so you are not paying for an idle cluster between them.
A serving layer engineered to clear large document sets on deadline, at a cost you can put on a matter.
Value concentrates wherever a large body of documents has to be cleared on a deadline, at a cost a matter can carry:
Document review is a throughput problem, not a latency one — the question is how many documents per hour the cluster clears, not how fast a single answer returns. We maximize tokens per second per GPU with continuous batching, paged-attention KV-cache, and quantization, then parallelize the review pipeline so a multi-million-document set finishes inside the production window. We size the serving layer to the corpus and the deadline, and we measure throughput against both.
Because legal work is billed and budgeted by the matter, we track cost the same way — cost per token rolled up to cost per matter, so a review has a predictable inference bill. We drive that number down with high GPU utilization, INT8/INT4 quantization validated against your review accuracy bar, and right-sized model routing so straightforward documents never touch an expensive model. Bursty matters that arrive on a litigation timeline are handled with autoscaling and queueing so you pay for capacity when a matter needs it, not around the clock.
Bring the corpus size, the production deadline, and the budget the matter carries. In thirty minutes we will show how high-throughput serving clears it inside the window — and how we will measure throughput and cost per matter. 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: