I will optimize your llm app for lower latency and cost

I optimize LLM applications that are too slow or too expensive.

What I have done in production:

- Cut p95 latency 70 percent and serving cost 38 percent with context caching, structured output and model routing

- Reduced output tokens 49 percent without losing quality

- Sped up list endpoints 125x and a threads query from 411ms to 1.6ms

- Re-homed three LLM and embedding models to an on-premise DGX with zero downtime

How it works:

1. You share your prompts, traces, model config and your latency or cost numbers

2. I profile the pipeline and send a prioritized report

3. I implement the fixes and show before and after benchmarks

Stack: OpenAI, Anthropic, vLLM, LangChain, RAG, pgvector, Python, TypeScript, Rust, Go, AWS.

Tell me your current p95 and monthly spend and I will tell you what is realistic.