High-scale AI infrastructure
Once a prototype ships and traffic finds it, latency and unit economics become the product. We make the boring parts work.
Inference cost reduction
There is almost always a cheaper inference call that does the same job. Smaller models for routing. Distilled checkpoints for the long tail. Prompt compression. Speculative decoding when latency matters more than tokens. Often the right answer is to route across providers — Haiku or GPT-mini for the hot path, Claude Opus or Gemini 2.5 Pro for the escalation tier — under a single eval harness. We measure first; we cut second.
Multi-layer caching
Embedding cache, query-rewrite cache, completion cache, KV cache reuse. Each layer measured for hit rate as a release blocker.
Continuous batching
Self-hosted endpoints with continuous batching (vLLM, TGI). 3–8× throughput on the same hardware, latency budgets preserved.
Quantization that doesn’t regress
AWQ/GPTQ for self-hosted, gated by your evaluation set. We refuse to ship a quantization that costs more in quality than it saves in cost.
- Token-level latency histograms. Time-to-first-token and inter-token latency per route, per model, per percentile.
- Cost per request, attributed. Tagged by feature, customer tier, query class. The CFO can read the dashboard.
- Quality regressions live. Sampled outputs scored against an online eval; alerts on drift, not just on errors.
- Replay infrastructure. Every prod request is replayable in a sandbox. Incident response in minutes, not days.
llm.request.latency_ms:
type: histogram
tags: [route, model, percentile]
budget_p95_ms: 1500
llm.request.cost_usd:
type: counter
tags: [route, model, customer_tier]
llm.eval.score:
type: gauge
tags: [route, evaluation_suite]
alert_on: drop_gt_3pct_24hNeed to make a working prototype viable?
If your AI feature is shipping but the unit economics don’t pencil out, or the latency story isn’t there, this is what we do.