Systems Architecture
AI System Architecture.
AI workloads carry a hidden 4–5x cost multiplier beyond the API bill. We design inference pipelines, model serving patterns, and data architectures that keep AI costs predictable.
How We Approach It
Inference Cost Engineering
Model selection, quantization, batching strategies, and caching layers designed to minimize cost-per-inference without sacrificing output quality. We treat inference as an engineering problem, not a fixed cost.
Data Pipeline Architecture
RAG pipelines, embedding stores, and data preprocessing flows designed for throughput and cost efficiency. Vector databases, chunking strategies, and retrieval patterns tuned to your specific workloads.
Build vs. Buy Analysis
Not every AI capability needs a custom model. We evaluate when managed APIs, fine-tuned models, or self-hosted inference make financial sense — and design the architecture to switch between them as economics change.
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