What comes with a deployment. Training and support are available to customers and evaluation partners.
Step-by-step guides for the common workflows: capturing knowledge, building the model of normal, running root-cause analysis and counterfactuals.
For deployment, data residency, validation, integration and licensing, ask the team directly for a specific answer, not a generic FAQ.
Structured learning paths by role (from a first causal model to independently validating one) with progress tracked beyond a single onboarding engagement. Content is in development; request access to be notified as it publishes.
The concepts underneath the platform: identification, estimation, causal graphs, and why correlation-only methods fall short on long-tail industrial conditions.
Hands-on walkthroughs of the platform itself: capturing knowledge, building the model of normal, running root-cause analysis and counterfactuals.
Role-based sequences for operators, process engineers and data scientists, each ending in the skills that role needs to work independently.
Live, instructor-led sessions and a certification track for teams standardizing on the platform.
Role-based training for operators, engineers and data scientists so your team builds and validates causal models independently: no-code, low-code and high-code.
Platform overview and architecture, core capabilities, causal discovery and analysis, root-cause analysis, what-if simulation, the causal solution workbench, deployment and administration: 185 topics across 266 pages. Read the architecture and deployment guides on this site; enter your work email for the complete documentation PDF, and a copy is sent to you.
Bring the engineer who knows the process. We will walk through the causal model behind it, and set up training, documentation and roadmap access.