A fraud score that arrives after the authorization decision is worthless, and a trading signal that lands a hundred milliseconds late has already missed the trade. We engineer low-latency model serving that holds its SLA at high concurrency, controls cost per token at scale, and runs on-prem so sensitive financial data stays inside.
Financial inference lives on a clock. A model deciding whether to approve a card transaction has a few milliseconds inside the authorization window before the network times out. A risk engine repricing exposure during a volatile open has to keep up with the tape. These are not "fast enough" targets — they are hard SLAs where the tail latency, not the average, is what breaks the business. And they have to hold while thousands of requests arrive at once.
At the same time the volume is enormous. A payments processor or a bank runs millions of inferences a day, so cost per token stops being a rounding error and becomes a number the CFO asks about. We treat inference here as an engineering problem with two hard constraints at once — a latency SLA that cannot slip and a unit cost that has to stay defensible — and build the serving layer to satisfy both, inside your own environment.
A serving layer engineered to the SLAs and volumes financial workloads actually run at — measured, not asserted.
Value concentrates wherever a model sits on a latency-critical path or runs at a volume large enough to move the cost line:
Yes. Fraud scoring at authorization time and signal generation on a trading path measure their budget in single-digit to low-double-digit milliseconds, so we engineer the serving layer to that target — tuned time-to-first-token, continuous batching that does not stall short requests, paged-attention KV-cache, speculative decoding, and request scheduling that protects the tail latency under concurrency. We load-test against your real transaction shape, including burst windows, so the SLA holds at peak rather than only in a quiet benchmark.
Financial workloads run millions of inferences a day, so cost per token is a real line item. We raise GPU utilization with continuous batching, shrink the compute footprint with INT8/INT4 quantization validated against your accuracy bar, reuse KV-cache to avoid recomputing context, and route cheap requests to small models so an expensive model is only invoked when it is warranted. Cost per token is tracked directly, and the whole stack can run on-prem so sensitive financial data never leaves your environment.
Bring the model you are running and the latency SLA and volume it has to hold. In thirty minutes we will show where the latency, concurrency, and cost-per-token wins are — and how we will measure them on infrastructure you control. 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: