Inference management for Legal.
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.
The metric is documents per hour, not milliseconds
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.
Built for throughput, cost, and bursts.
A serving layer engineered to clear large document sets on deadline, at a cost you can put on a matter.
- Continuous / in-flight batching
- Parallelized review pipeline
- Throughput sized to deadline
- Cost-per-matter rollup
- Quantization to accuracy bar
- Right-sized model routing
- Scale-to-load autoscaling
- Queueing & backpressure
- On-prem inference
Where inference management unlocks value in Legal
Value concentrates wherever a large body of documents has to be cleared on a deadline, at a cost a matter can carry:
- eDiscovery and review — millions of documents classified, summarized, and tagged at sustained throughput, so a production set clears inside the deadline instead of overrunning it.
- Contract analysis — large contract portfolios processed for clauses, obligations, and risk as efficient batch, with cost attributable to the deal or matter.
- Due diligence — transaction data rooms reviewed under acquisition timelines, with serving that scales to the burst and stands down afterward.
- Privileged-material safety — the entire pipeline runs on-prem, so privileged and confidential documents never leave the firm or the client's environment.
Common questions.
How do you serve LLM inference fast enough for large-scale document review?
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.
How do you keep cost per matter under control?
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.
Explore related paths.
Clear the review on deadline and on budget.
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.
Experienced within
Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: