Hands on AI and machine learning training built around your domain, your tools, and your real production constraints, not generic curricula and quiz shaped certificates.
Most AI training is a stack of generic videos and a multiple choice exam at the end. Learners go back to work and discover that their codebase, their data, and their constraints look nothing like the ones in the course. We build the curriculum around your actual environment, and the capstone is something the business actually needs.
Programs range from executive briefings for leaders who need AI literacy without writing code, to engineering bootcamps for ML practitioners who need to ship models that survive production. Depth, language, and hands on work are calibrated to the audience in front of us.
Every program is designed and delivered by senior engineers with production scars, the same people who would lead a project in your organization. No junior trainers reading a script. No AI 101 deck recycled from a conference talk. The deliverable is a team that can build the systems you previously had to outsource.
We tune the depth, the tooling, and the language to the people in the room. These are the most requested formats, though every engagement gets shaped to fit.
A focused half day or two day session for executives, board members, and senior decision makers. We cover what AI can and can't do, where the regulatory and reputational risks live, how to read a model evaluation, and how to ask the right questions of your technical team. No code, plenty of judgment.
A 12 week intensive for software engineers and data scientists moving into production ML. Curriculum covers the full stack: data pipelines, modeling, deployment, monitoring, and on call ownership. Capstone projects are graded on real data, not toy datasets, by senior engineers.
Healthcare AI, Defense ML, Finance modeling, Manufacturing predictive maintenance, Public Sector LLMs. Curriculum, datasets, and case studies built around your sector, including the regulatory and ethical considerations that come with the territory.
Bring an entire team or department through the same curriculum at the same time, with content adapted to your codebase. Everyone learns the same language, the same patterns, and the same conventions, so the team that finishes the program looks like a team, not a collection of individuals.
Ongoing one on one and small group coaching for engineers and leaders past the formal training. Bring real problems, real code, real production incidents, and get help from senior practitioners who have shipped through them before.
Before and after assessments that measure actual skill, not seat time. We design role specific evaluations, design review, code review, model review, on call drill, and produce a defensible signal of who's ready for what kind of work.
Three representative engagements, the audience, the problem, and the capability that team walked away with.
A clinical AI team needed to use large language models on patient data without breaking HIPAA, leaking PHI, or hallucinating diagnoses. We delivered a four week curriculum covering RAG patterns, evaluation harnesses for medical accuracy, prompt injection defenses, and an audit trail design that satisfied legal review.
A manufacturing operations team had a vendor supplied predictive maintenance model and zero idea how it worked. We ran a six week program that covered the underlying ML, how to read its outputs, when to override it, and how to detect when the model itself was drifting away from reality.
A bank's risk and compliance team was asked to sign off on ML models without the technical vocabulary to do so. We built a custom curriculum covering model documentation, fairness testing, monitoring, and the questions a compliance reviewer should ask before a model goes live.
Bad corporate training is everywhere, and most of it fails for the same reasons. These five rules are how we stay on the right side of that line, in every program we deliver.
We build the curriculum around your codebase, your data, and the systems your team is actually trying to ship. The capstone is something the business needs, not a toy dataset. There's no quiz shaped certificate at the end, there's a working artifact in your repository.
Senior engineers and researchers who have shipped production AI systems, the same people who would lead an engagement in your organization. No junior trainers reading from a script.
Yes, and we usually do. We sign NDAs and work inside your environment for any code that doesn't leave your network. The curriculum is built from the code you point us at; the examples in lectures are your code, not a tutorial.
Cohorts of 8 to 24 work best for hands on engineering bootcamps. Executive briefings can run larger (40 to 80 leaders) because they're seminar format. Outside those bands we either split into cohorts or restructure into a different format.
We don't issue certificates because the industry is flooded with them and most are worthless to a hiring manager. What you get instead is a written assessment of each learner's capability and the working artifact they built, which is what a hiring manager actually wants to see.
Tell us the audience, the gap, and the outcome you need by when. We'll come back with a one page program proposal: agenda, instructor, capstone project, and the measurable capability the team will walk away with.