Senior Engineers, Not Layers of Mediation
Direct access to engineers who can inspect, decide, and execute.
What We Solve
Make AI features economically durable.
Many teams discover the hard truth quickly: model quality alone does not create a business. 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, avoidable retries, missing caches, and the absence of observability around token and latency behavior.
- 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 where AI enthusiasm becomes 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 Choose SToFU When Stakes Are High
Senior engineering. Clear decisions. Real outcomes.
Commercially Useful Outputs
Scope, priorities, remediation, and next steps your team can use immediately.
Built for AI-Era and High-Stakes Systems
AI-native platforms, native software, secure systems, and low-latency infrastructure.
Share the system, the pressure, and the deadline. We will turn that into a concrete next move.