Empirical systems design, autonomous agent workflows, and high-throughput RAG infrastructure orchestrated across the world's leading frontier foundation models.
End-to-end engineering from data pipelines and retrieval backends to autonomous agent orchestration and edge delivery.
Multi-agent architectures with state machines, deterministic tool calling, dynamic plan reflection, and fail-safe recovery patterns.
Multi-vector dense & sparse indexing, Cohere/BGE cross-encoder reranking, and semantic knowledge graphs for grounded factual accuracy.
LoRA, QLoRA, and Direct Preference Optimization (DPO) pipelines to align open-weight models (Llama 3, Mistral, DeepSeek) to proprietary workflows.
Ultra-low latency serverless inference using Cloudflare Workers AI, Vectorize, and Workers KV for globally distributed edge compute.
Rigorous automated evals, red-teaming, PII masking, latency benchmarking, and real-time semantic guardrails preventing prompt injection.
Dynamic cost-aware and latency-aware routing that automatically dispatches tasks to the most cost-effective model without sacrificing fidelity.
Simulate real-world token volume, speed requirements, and cloud model expenses across premier foundation model providers.
Real-world implementations delivering high throughput, sub-second latency, and airtight security.
Engineered hybrid sparse/dense retrieval across 10M+ SEC filing pages with sub-180ms P95 query response time and zero hallucinations.
Orchestrated a 3-agent pipeline performing AST parsing, test synthesis, and semantic code patch verification on enterprise GitHub pull requests.
Designed an air-gapped on-premise inference cluster with PII redactors and DICOM imaging parsing for clinical research workflows.
Whether you are designing a greenfield generative AI product or modernizing existing high-volume LLM pipelines, get direct architecture guidance from Nael.