Hands-on training that takes your team from AI-curious to confidently building, validating, and shipping physics-aware agent workflows — on your own tools and data, in your own domain.
No data-science background required. We meet your engineers where they are and get them productive with agentic AI on real work.
Reservoir, power, and simulation engineers who want to automate scenario analysis and reporting without becoming ML experts.
Team leads and managers who need their engineers productive with agentic AI fast — and want governance and traceability set up right from day one.
Specialists who want to wrap their models, scripts, and pipelines as agent tools the wider team can safely reuse.
A practical curriculum, tailored to your domain and tools. We teach the concepts and build real workflows alongside your team.
What agents actually are, when they help, and — just as important — where they fail. How agentic AI differs from chatbots and generic automation.
Connect your tools, wire multi-agent workflows, and orchestrate specialist agents for data prep, analysis, ranking, and reporting.
Run your simulations and scripts as agent tools, reason over real outputs, and work with uncertainty ranges (P10 / P50 / P90) instead of single guesses.
Build audit trails, add human-in-the-loop approval, and produce defensible outputs your team — and your regulators — can trust.
Practical and hands-on, delivered remotely or on-site, and built around your own data so the skills transfer straight to real work.
Interactive sessions — remote or on-site — that mix concepts with live building, not slideware.
Your engineers build real workflows using your own simulations, scripts, and datasets.
Examples and exercises drawn from your field — subsurface, power systems, or simulation-heavy engineering.
Post-training support and office hours so the team keeps shipping after the workshops end.
By the end, your team isn't just AI-aware — they can build and defend real engineering workflows.
Build a working multi-agent workflow. From a blank canvas to a running workflow that connects tools, reasons over results, and produces an output.
Wrap their own tools as agent skills. Connect simulations, Python scripts, and data so agents reason over real engineering numbers.
Ship outputs they can defend. Quantify uncertainty, add human gates, and produce traceable recommendations — and know when not to use an agent.