Inference management for Financial Services.
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.
Where the latency budget is measured in milliseconds
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.
Built for latency, concurrency, and scale.
A serving layer engineered to the SLAs and volumes financial workloads actually run at — measured, not asserted.
- Time-to-first-token tuning
- Tail-latency scheduling
- Speculative decoding
- Continuous / in-flight batching
- Queueing & backpressure
- Burst-window load testing
- Quantization to your accuracy bar
- Cost-per-token tracking
- On-prem inference
Where inference management unlocks value in Financial Services
Value concentrates wherever a model sits on a latency-critical path or runs at a volume large enough to move the cost line:
- Real-time fraud scoring — models return a decision inside the authorization window, so legitimate transactions clear and fraudulent ones are stopped without adding perceptible latency at the point of sale.
- Trading and market signals — inference keeps pace with the tape, with tail latency held tight enough that a signal is still actionable when it lands.
- Real-time risk and exposure — risk engines reprice and re-score continuously through volatile windows without the serving layer becoming the bottleneck.
- High-volume document and KYC processing — batch and streaming inference over statements, filings, and onboarding documents at a cost per token that stays defensible across millions of items.
Common questions.
Can LLM inference hit the hard low-latency SLAs that fraud and trading require?
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.
How do you control cost per token at the volume a bank runs?
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.
Explore related paths.
Make every inference fast and affordable.
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.
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: