fastpriorsTalk to an engineer
services

Ten things we do.
Done well.

Every engagement is fixed-scope. We start with a written cost audit; you decide whether the math works before we touch production.

01 /

AI Infrastructure Migration

From OpenAI, Anthropic, Together, Replicate, Bedrock, or Vertex onto your own GPUs in your VPC. We design the cutover plan, run shadow traffic, validate eval parity, and stage the rollout behind feature flags so customers never notice.

Migration planShadow traffic harnessEval parity reportRollback runbook30-day warm period
Duration
6–14 weeks
Typical outcome
median 73% cost reduction
02 /

Inference Optimization

Speculative decoding, paged attention, INT8/FP8 quantization, kernel fusion. We profile your actual production traffic, then commit to a target — p95 latency, tokens/sec, $/1M — and ship until we hit it.

Profiling reportRuntime selection (TRT-LLM/vLLM/SGLang)QuantizationBatching strategy
Duration
4–8 weeks
Typical outcome
median 4.2× throughput uplift
03 /

Custom CUDA / Triton kernels

When the off-the-shelf doesn't fit. Fused ops for unusual attention variants, sparse MoE routing, novel KV cache layouts, custom samplers. Profiler-led, math-first.

Kernel specReference impl + testsBenchmark suiteDocs + handover session
Duration
3–10 weeks
Typical outcome
1.4–6× kernel-level speedup, typical
04 /

GPU Hardware advisory

What to buy, when to rent, when to colo. We've sized clusters from a single workstation to multi-thousand-GPU H200 fleets. Vendor-agnostic — we have no resale agreements.

Workload sizingSKU comparisonPower & networking specProcurement timeline
Duration
2–3 weeks
Typical outcome
20–40% capex reduction, typical
05 /

Inference Scaling & Autoscaling

Multi-region deployment, request routing, cold-start mitigation, traffic shaping. Built on what you already run — k8s, Nomad, bare metal — not a proprietary platform.

HPA + VPA tuningCold-start playbookTraffic routingCapacity planner
Duration
4–6 weeks
Typical outcome
≥99.95% uptime, p95 SLO met
06 /

RAG Performance Tuning

End-to-end retrieval pipelines that don't blow your latency budget. Embedding model choice, vector store sizing, hybrid retrieval, reranker placement, batching across stages.

Retrieval evalReranker selectionPipeline rewriteLatency budget
Duration
3–6 weeks
Typical outcome
sub-50ms p95 RAG, typical
07 /

Model Distillation & Quantization

Take a 70B teacher down to a 7B student that holds eval parity within tolerance. INT4/INT8/FP8, GPTQ, AWQ, LLM-QAT — whatever the hardware likes best.

Eval harnessDistilled checkpointQuantization studyDeployment package
Duration
4–8 weeks
Typical outcome
5–10× cost-per-token reduction
08 /

On-prem / VPC Deployment

Air-gapped, ITAR, HIPAA, EU data residency — whatever your compliance regime requires. We deploy and document everything; your team operates it.

Reference architectureTerraform modulesOps runbooksAudit-ready docs
Duration
6–10 weeks
Typical outcome
audit-ready inference stack
09 /

SRE & On-call (limited)

Three-month bridge contracts only. We share on-call rotation while your team learns the system. Then we leave, on schedule.

On-call rotationIncident playbooksPostmortem templatesKnowledge transfer
Duration
up to 12 weeks
Typical outcome
your team owns it after Q1
10 /

Cost audit

Written diagnostic. We inspect your stack, model your spend, and tell you in writing how much you'd save self-hosting. Refundable against the engagement that follows.

Spend modelMigration estimateRisk assessmentWritten recommendation
Duration
~ 1 week
Typical outcome
decision-grade analysis

Not sure which one you need?

The cost audit usually answers it — refundable against whatever comes next.

Start with an audit →