LLM Optimization & Generative Engine Services
Maximize Large Language Model throughput, decrease latency, eliminate prompt injection vulnerabilities, and dominate Generative Engine Optimization (GEO) & LLM SEO.
What this covers
- Inference Quantization (GGUF, AWQ, EXL2) & vLLM Acceleration
- Embedding Caching, Vector Indexing & Hybrid RAG Retrieval
- Prompt Injection & Jailbreak Penetration Testing
- LLM Leaderboard & Competitive Benchmark Analysis
- Generative Engine Optimization (GEO) & LLM SEO Strategy
- Model Distillation into Efficient Small Language Models
What we build on
Model choice is a dependency, not an identity. We keep the provider swappable and evaluate before switching.
- Claude
- GPT
- Gemini
- Llama
- Whisper
- Embeddings
- Vector search
- RAG
- Function calling
- Evals
Where this is heading
Deploying a foundation model is trivial; running LLMs in production at sub-100ms time-to-first-token while keeping GPU spend under control is the true engineering challenge. We optimize every layer from model quantization to structured context compression.