The AI Stack We Actually Ship in 2026

Most "AI" you see in demos never survives contact with real users. The gap between a slick chatbot and a system that answers correctly, every time, on your data — that gap is the whole job. Here is the stack we actually ship in 2026, and why.

Orchestration: LangGraph over loose scripts

Agents that matter aren't a single prompt — they're graphs of steps with retries, branches and human checkpoints. We build them with LangGraph (and LangChain for the glue) because explicit state beats clever prompting at 3am when something breaks.

Retrieval: RAG, and GraphRAG when relationships matter

Plain RAG with a vector database covers most cases. We default to Qdrant or pgvector for retrieval. But when the answer depends on relationships — org structures, dependencies, citations — we reach for GraphRAG, which traverses a knowledge graph instead of hoping a flat similarity search connects the dots.

Serving: private models with vLLM and Ollama

Not every workload should call a third-party API. For sensitive data we serve open models with vLLM (throughput) or Ollama (simplicity), so prompts and outputs never leave infrastructure we control.

Tooling: MCP and Pydantic AI

The Model Context Protocol (MCP) is the part of 2026 that will age well — a clean, standard way to give agents tools and context. Paired with Pydantic AI for typed, validated outputs, it turns "the model said something" into "the model returned a structured object we can trust."

The part most teams skip: where it runs

Every one of these workloads runs on our AWS infrastructure in EU regions, processed under GDPR. Your data — and your customers' data — never leaves Europe. For most of the companies we work with, that single fact decides the project.

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