Northbound AI Summit 2026

AI engineering stopped being a frontier discipline and started being a job description — but the tools, the org charts, and the failure modes are still catching up. This opening keynote takes stock of where production teams actually are, dr…

Prompt injection is no longer a party trick — it is a production security class with real incidents, and most mitigations teams reach for first do not survive contact with an attacker. This deep dive works through the attack taxonomy as it…

Retrieval went from research afterthought to the load-bearing wall of production AI, and most of what made it work for us was not in any paper. This keynote traces one retrieval stack over three years: naive vector search, the hybrid rebuil…

Our inference bill was on track to pass our payroll. This talk is the story of cutting tokens-per-dollar by 7x without a visible quality drop, in the order the savings actually arrived: response caching, prompt-prefix reuse, routing easy qu…

Bring a laptop, leave with a model. In 90 minutes we take a 3B-parameter open model from base weights to a fine-tune that beats a model ten times its size on one specific task — classifying and routing support conversations. The workshop c…

Vector search is a great first chapter and a terrible whole book. In production, the retrieval systems that hold up are hybrids: lexical search for precision, embeddings for recall, structured filters for correctness, and a reranker to arbi…

Evals are the highest-leverage infrastructure an AI team can own, and the tooling matters far less than the muscle. In this hands-on session we build an eval harness from an empty directory: golden sets, graders (exact, rubric, and LLM-judg…

Four teams run open-weight models in production today, at four very different scales, and none of them made the same choices. This panel puts their engineering leads on one stage to compare notes for real: serving stacks, fine-tuning strate…