Latency engineering
Streaming, caching, parallelism and model right-sizing that turn sluggish AI features responsive.
Engineering Excellence
AI Performance Optimization
Cut spend and speed up responses with caching, routing, distillation, and architecture tuning.
- 40–70%inference cost reduction
- 3–5×latency improvements on tuned paths
- 0quality regressions — every change eval-gated
Capabilities
Faster, cheaper, provably as good
Optimize latency, cost and throughput for production AI systems.
Cost optimization
Token diets, routing and caching that bend the inference curve without bending quality.
Throughput and scaling
Batching, concurrency and serving optimizations for high-volume workloads.
Quality assurance
Every optimization validated against evals — savings that don't cost you accuracy.
How it works
Performance is engineering, not settings
AI performance problems hide in prompts, retrieval, serving and architecture simultaneously. We profile the whole path and fix what matters most first.
Profiling
Find where the time and money actually go
End-to-end tracing breaks every request into its parts — queueing, retrieval, model time, tool calls, post-processing. Optimization starts from measurement, not folklore.
- Request-path profiling across the full AI stack
- Cost attribution per feature, prompt and model
- Prioritized fix list ranked by impact
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Latency
From spinning cursor to instant
Streaming-first UX, prompt caching, speculative and parallel execution, smaller models on the hot path — applied where profiling says they'll pay, verified against quality gates.
- Prompt and semantic caching with measured hit rates
- Parallel tool and retrieval execution
- Model right-sizing per route with eval evidence
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
Cost
Bend the curve before finance does
Context trimming, output limits, batch processing, distillation of high-volume tasks onto fine-tuned small models — systematic reductions that compound, with dashboards proving the savings hold.
- Token diet: context and output engineering
- High-volume tasks distilled to cheaper models
- Continuous cost monitoring with regression alerts
Incoming taskcomplex reasoning · 12k ctx
Router
Claude Sonnetquality-critical
GPT-4.1fallback
Llama 70Bhigh volume · low cost
FailoverA/B evalsCachingBudgets
Use cases
Where performance optimization delivers value
Cost crisis response
Inference bills audited and cut fast when spend outruns plan.
Latency rescue
Slow AI features brought under interactive latency budgets.
Scale preparation
Systems hardened for 10–100× volume ahead of launches.
Voice and real-time AI
Sub-second budgets met for conversational experiences.
Batch pipeline tuning
Overnight jobs compressed to hours, costs cut in parallel.
Margin engineering
AI product unit economics rebuilt to support pricing.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
-
1
Discover
Map workflows, data, constraints and ROI.
-
2
Architect
Define models, tools, memory and trust boundaries.
-
3
Build
Ship a production-ready system with evals and observability.
-
4
Scale
Optimize cost, quality and adoption across teams.
P95 went from 4.1 seconds to 1.1, and monthly inference spend dropped 61% — with the eval suite proving quality never moved. Finance and users noticed the same week.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Anthropic Claude
- OpenAI
- vLLM
- Redis
- Groq
- AWS Bedrock
- Cloudflare
- Datadog
- Grafana
- OpenTelemetry
- Braintrust
- Kubernetes
FAQ
Common questions
How much can we realistically save?
Systems that haven't been through disciplined optimization typically yield 40–70% cost reduction and 2–5× latency improvement. An initial profiling engagement quantifies your specific headroom before you commit to the work.
Will optimization degrade output quality?
Not on our watch — that's the point of eval gating. Every change ships behind a quality gate on your golden set; anything that regresses beyond threshold is rejected, whatever it saves.
Should we just wait for cheaper models?
Model prices fall, but usage grows faster — and architectural waste scales with it. Teams that optimize their architecture bank the model-price declines instead of consuming them.
Where do you usually find the biggest wins?
Bloated contexts, absent caching, oversized models on simple tasks and serial execution that should be parallel. The top four fixes usually deliver most of the value in weeks.
Next step
Cut the bill, keep the quality
One profiling sprint tells you exactly how much latency and cost you're leaving on the table.