Senior Access
Speak with engineers who can inspect, decide, and execute.
What We Solve
Make AI features economically durable.
Response time, serving efficiency, and infrastructure discipline decide whether the feature survives scale.
We work where the waste hides: low GPU utilization, oversized models, weak routing, poor batching, and missing caches.
- Slow p95 and p99 that damage product experience
- Rising GPU spend with weak utilization and poor serving choices
- Wrong model routing that overpays for routine requests
- Inefficient batching and caching that waste throughput
- Autoscaling drift that increases cost without stability
- Opaque serving stacks with weak profiling and cost visibility
- Feature rollout pressure without a stable inference budget
- Architecture debt from pilots promoted directly into production
Inference optimization is operating discipline.
What You Get
- Serving architecture review for latency, throughput, and cost behavior
- Optimization plan across routing, batching, caching, and hardware placement
- Profiling visibility for tokens, requests, queues, and utilization
- Rollout strategy for safer scaling and performance regression control
- Cost model tied to product traffic and business constraints
Coverage and Delivery
Serving Stack
- Model serving architecture and engine selection
- Batching, caching, concurrency, and queue behavior
- Quantization and runtime optimization paths
- Model routing, fallback logic, and request shaping
Performance and Cost
- GPU and CPU placement strategy
- Latency breakdown and profiling methodology
- Utilization analysis and scaling policy review
- Budget-aware recommendations for production traffic
Typical Outputs
- Serving and routing architecture map
- Latency and cost bottleneck analysis
- Optimization roadmap with sequencing
- Monitoring and regression guard recommendations
Business Fit
- AI products approaching production scale
- Teams with rising inference spend and unstable response times
- Platforms where margins depend on serving efficiency
- Organizations that need AI capability without runaway infrastructure cost
Why Teams Move Fast
Senior engineers. Clear next steps. Work built for systems that carry real pressure.
Personal data is handled with clear discipline across GDPR, UK GDPR, CCPA/CPRA, PIPEDA, and DPA/SCC expectations where applicable.
Usable First Step
Reviews, priorities, scope, and next moves your team can use right away.
Built for Pressure
AI, systems, security, native software, and low-latency infrastructure.
Delivery
Senior-led
Direct technical communication
Coverage
AI, systems, security
One team across the stack
Markets
Europe, US, Singapore
Clients across key engineering hubs
Personal data
Privacy-disciplined
GDPR, UK GDPR, CCPA/CPRA, PIPEDA, DPA/SCC-aware
Start with the system, the pressure, and the decision ahead. We shape the next move from there.