Hands-on AI training for engineering teams and senior leadership. Cohort-based, project-based, built around the systems your people are actually trying to ship.
Most AI initiatives don't fail in the model. They fail in the team that has to operate it. Engineers who never built a RAG pipeline get handed one and asked to make it reliable. Leaders making nine-figure platform decisions are working from vendor pitches and analyst reports.
Our training practice fixes that gap. We run cohort programs that give your engineering teams real production experience and your leadership team a working mental model. Each program ends with an artifact your organization keeps.
A full cohort program for engineering teams shipping their first or second production AI system. Participants leave with a working pipeline they built, an evaluation harness they own, and runbooks for operating it.
Three days for senior leaders responsible for AI strategy. Working sessions, not slide decks. Participants leave with a written strategy, a portfolio map, and a vendor diligence framework calibrated to their industry.
Designed for organizations with a specific stack, regulation, or platform decision in flight. We build the curriculum on your data, on your tools, with your real use cases. Not a recycled course.
Targeted formats for boards, leadership offsites, or specific teams. Pick a topic and we run a single intensive session that ends with a written takeaway document.
Six weekly modules plus a final capstone week. Each week is half lecture, half hands-on lab.
Tokenization, context windows, prompt design that actually works. Cost models. Vendor selection.
Chunking strategies, embedding models, vector stores, and the failure modes that bite in production.
Tool design, planning, multi-step reasoning, and the operating challenges of long-horizon tasks.
Eval harnesses, regression tests, drift detection, and how to know whether your system is still working.
Threat modeling for LLM systems, regulatory pattern recognition, and a practical red-team protocol.
Deploy, monitor, version, and roll back. What an on-call rotation actually looks like for AI systems.
Each team takes a real internal use case from brief to production deployment. The artifact is yours, the runbook is yours, the lessons are written down.
First or second production AI system, working with regulated data, accountable to a real on-call rotation.
Boards, exec teams, and CIOs/CTOs making nine-figure platform decisions and needing a working mental model.
Risk, compliance, security, product. Teams that need to engage with AI work without becoming engineers.
Tell us about your team, your stack, and what you want them to be able to do at the end. We'll come back with a curriculum and a timeline.