Enterprise Productivity and AI Solutions

For programs that need to go beyond the built-in tools, GTE's consultants take on project work that blends physics-based simulation with custom machine learning: surrogate modeling, data-driven model reduction, and workflow automation tailored to a specific product or program. That scope extends to GT-Play implementation and GT-Automation scripting and deployment, so teams get both the modeling and the tooling that runs it in production.

Answers are only as good as the knowledge base behind them. GTE works with customer teams to structure and prioritize the documentation and modeling standards AI.advisor draws on, so answers reflect how that specific team actually works, not just generic GT-SUITE documentation.

Turning a PDF, an engineering image, or an existing model into a working GT-SUITE build still takes judgment calls. GTE guides teams through that workflow directly, reviewing outputs and correcting setup so model quality holds up as the pace increases.

Generating a script is one thing, folding it into an existing simulation pipeline is another. GTE helps teams identify where AI.coder fits into their workflow and builds out the automation around it, so the time saved on scripting doesn't get lost re-integrating the output.

GTE helps teams implement GT-Play, publishing existing GT-SUITE models to the platform and configuring them for organization-wide use. Beyond the setup, GTE trains teams on how to work within GT-Play day to day, so colleagues across the organization can run and interpret simulations independently. The result is effective, efficient collaboration that extends simulation capability well beyond the group that built the original models.

GTE builds and deploys GT-Automation scripts so they hold up across teams and projects, then trains users to run and adapt them without GTE staying in the loop.