I build systems where a language model is one component among several: LangGraph agents with tool use and memory, retrieval pipelines on Qdrant, protocol-based tooling on MCP, and the FastAPI and React applications that put them in front of people.
Most of what I know is in the case studies. Kylie started as a WhatsApp agent with hand-wired tools and became an MCP-based system whose capabilities are discovered at runtime and traced end to end in Opik; Qdrant featured the original build. The Visual Document Understanding Pipeline turns PDFs and photos into clean Markdown: a layout model finds each region, Qwen3.5-4B on vLLM reads it, the GPU pools scale to zero, and an MCP server lets Claude read documents too.