Inference Optimization & AI Infrastructure
Find the operating point between quality, latency, throughput, and cost.
Who commissions this
A defined technical problem.
Teams facing slow responses, GPU capacity limits, unpredictable serving costs, or infrastructure decisions for an AI workload.
Engineering scope
What the work covers.
- Profile the full request path, separating queueing, retrieval, prefill, decoding, and tool execution.
- Measure p50/p95 latency, time to first token, throughput, memory, and cost per accepted task.
- Compare batching, caching, quantization, routing, and serving configurations against a fixed quality gate.
- Stress-test concurrency, long contexts, burst traffic, failures, and capacity assumptions.
Representative deliverables
Work your team can inspect and use.
- Workload trace specification and reproducible benchmark
- Measured bottleneck analysis and configuration comparison
- Capacity and cost model with stated assumptions
- Rollout plan with quality gates and rollback criteria
A measurable outcome, scoped together.
An optimization decision supported by your workload. No universal speedup or cost reduction is promised.
Typical engagements begin at $50,000. Before a proposal, we agree the business objective, data access, system boundaries, deliverables, acceptance criteria, and operating owner. Timelines depend on scope and access.
Related technical evidence
Production AI architecture and inference economics · How to verify an AI performance claim
Explore all engineering services · Independent technical assurance