FDE Path
Follow the full D.Hub resource-building flow, from data collection and ontology modeling to pipelines, AI agents, dashboards, and final validation.
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About this Path
This Path is an onboarding guide for Forward Deployed Engineers (FDEs) and product teams who build customer-facing solutions on top of D.Hub. It follows one end-to-end working flow: isolate a workspace, model an ontology, build pipelines, configure agents, connect tools and knowledge, and finish with a deployed experience.
The Path is organized as six lessons that follow the actual portal flow and screen sequence. Together, they give you one practical pass through how the main D.Hub resources fit together.
Prerequisites
- A D.Hub portal account with permission to create resources
- Finishing the Essentials Path first is recommended if you want a smoother start with the example flow
What you'll be able to do
- Understand how collections isolate workspaces and hold the main resources used in a solution
- Define entities and relationships in the Ontology Builder and shape a knowledge graph
- Build ETL pipelines that load data into datasets and ontology entities
- Turn datasets and ontology outputs into operational dashboards and widgets
- Register LLM models, configure agents, and connect tools and actors
- Attach data connections, knowledge bases, and RAG, then validate the deployed agent
What comes after this Path
If you want to continue with a domain-specific scenario, move on to a workshop:
- Workshop: Retail Inventory Intelligence — Build a full scenario from pipeline setup to dashboard operations using realistic data
Check off each lesson as you finish it — progress is recorded automatically. Pick one up and start.
Lessons
- 01Collection managementCreate a collection and add resources as the first step in isolating workspaces and organizing solution assets in D.Hub.6 min
- 02Ontology modelingConnect multidimensional raw data into a structured semantic model and design the backbone of a knowledge graph.8 min
- 03Pipeline build and dashboard visualizationLoad and transform raw data, then connect the result to dashboard widgets and complete an operational screen.10 min
- 04Model registration and agent setupRegister an LLM model, connect system instructions and operating mode, and assemble an agent for real use.9 min
- 05Tools, connectors, and knowledge integrationConnect tools and data access, then add a knowledge base so the agent has the execution resources it needs.10 min
- 06Deployment and final validationDeploy the completed agent and verify its knowledge and tool integration from the chat interface.7 min