AI Infrastructure

Self-hosted or managed inference, vector stores, retrieval pipelines, and observability for production AI.

The gap between a working AI prototype and a production-ready system is where most projects stall. A model that performs well in a Jupyter notebook under controlled conditions is not the same as a reliable, cost-predictable service that handles real user load, degrades gracefully when something goes wrong, and gives your team the observability to understand what it is doing. Building that production-grade AI infrastructure is what I do. The foundation is inference. Whether that means calling a hosted API with the right retry, timeout, and fallback configuration, or deploying a self-hosted model on your own hardware or cloud environment, the infrastructure needs to be designed from the start for reliability and cost control. For businesses in Germany with strong data residency requirements, self-hosted inference removes the question of where data goes entirely — it stays on your servers. Vector databases and retrieval-augmented generation are central to most of the AI systems I build. Embedding-based retrieval allows AI systems to work with your actual business knowledge — documentation, product data, customer histories, regulatory texts — rather than relying on what a base model learned during training. I implement retrieval pipelines using Qdrant, Weaviate, or pgvector depending on the scale and infrastructure context, with chunking strategies and embedding models tuned for the specific retrieval task. Caching is one of the most overlooked parts of AI infrastructure and one of the most impactful for cost and latency. Semantic caching layers can reduce LLM call volume dramatically for systems with repetitive or similar inputs, while prompt caching cuts the cost of long context windows. Observability is non-negotiable in production AI systems. I instrument every system with structured logging of prompts, model responses, latency, cost per call, and downstream actions. For agentic systems, I log the full decision trace. This is what enables you to debug unexpected outputs, measure quality over time, catch regressions after model updates, and build confidence in the system's behavior. Evaluation frameworks close the loop. Before any AI system goes to production, I build an eval harness with representative test cases, quality metrics relevant to the task, and a repeatable process for regression testing when models or prompts change. This is the engineering discipline that makes AI systems maintainable — not just impressive in demos.