Hands-on AI training for engineering teams, senior leadership, and individual practitioners. 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.
Individual seats, full materials: weekly lectures, graded exercises, projects, and a certification exam. Every course qualifies you for the PCAIP credential.
The on-ramp. From the history of AI through working ML models to generative AI and organizational strategy. No prior AI or programming experience required; benchmarked against MIT and Stanford intro curricula.
For software engineers integrating AI into production systems. 24 live sessions, 85+ coding exercises, 8 graded projects ending in a deployed capstone. Python fluency assumed.
AI for risk, trading, and compliance teams. Credit and market risk models with explainability, fraud detection, deep-learning forecasting, and the RegTech stack, built to regulatory standards like SR 11-7 and CCAR.
AI for supply chain, logistics, and plant teams. Demand forecasting, network and inventory optimization, routing, predictive maintenance, and edge deployment, with industry-standard tooling like OR-Tools and SimPy.
Hybrid cohorts: in person in Frisco, live on Zoom, recordings kept. Both run on the Proxiant Datacenter. Day 1 is a free open day.
For engineers already working with LLMs. Reads the research, writes the training loops, ships a real capstone: an agentic application with an MCP server, a fine-tuned model, and a 50-example eval suite. Sundays plus two evening lab sessions a week.
Hackathon-style and coding-centered: teams of 4 to 6 build production retrieval systems in dedicated rooms, Saturdays on campus plus two evening lab sessions a week. From embedding geometry to a fully evaluated enterprise RAG stack.
Weekly cadence for both: one full theory-and-lab day, two guided evening lab sessions, a weekly quiz, paper readings, and team project presentations. Grading runs across quizzes, projects, labs, paper presentations, a capstone, and a final certification exam.
The Proxiant Certified AI Professional exam validates applied AI skill. Passing any Academy course with 70 percent or better qualifies you to sit it. Bootcamp graduates earn the specialized PCAIP-Agents and PCAIP-RAG credentials.
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.
Engineers and analysts taking an open-enrollment seat: full course materials, graded projects, and a path to PCAIP.
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. Or take a seat in the fall cohorts: the RAG bootcamp starts October 3, 2026, and the agents bootcamp starts October 4.