Documentation
Build Sequence
The target architecture does not change as rFabric expands. What changes is how much depth exists inside each backbone plane and product component. The sequence below shows how the platform compounds from the current wedge outward.
Phased Expansion
Phase 1 - Data foundation wedge
Establish the system-of-record position around robot data intake, labeling, and dataset preparation.
- Data Ingestion & Preprocessing
- Annotation & Labeling
- Dataset Curation & Versioning at baseline depth
- Unified Data Model & Traceability for core data entities
- Platform API, CLI & SDKs for data operations
- Identity & Access and Governance & Tenancy at baseline depth
Phase 2 - Dataset quality depth
Deepen the highest-leverage surface in robot learning: dataset quality, curation ruleset, and review rigor.
- Automated quality scoring and policy-driven selection
- Multi-operator coordination and correction loops
- Orchestration for data-to-dataset automation
- Governance policy depth for retention, regions, and approval paths
- Metering & Billing for storage and media-processing economics
Phase 3 - Model development
Extend trust from governed datasets into reproducible training runs, evaluation results, and promoted model identity.
- Model Training & Evaluation
- Model Registry
- Training lineage inside the shared data model
- Orchestration for dataset-to-model workflows
- Compute visibility through Metering & Billing
Phase 4 - Release management
Turn approved models into governed production artifacts and controlled rollout flows.
- Release Packaging
- Deployment & Updates
- Environment progression and deployment approvals
- Release and deployment entities added to the shared graph
- Promotion, rollout, and rollback flows orchestrated end-to-end
Phase 5 - Live operations
Complete the closed loop from deployed fleets back to data, evaluation, and better future releases.
- Fleet Management
- Monitoring, Alerting & Maintenance
- Human Intervention & Robot Control
- Operations lineage across telemetry, incidents, maintenance, and interventions
- Full lifecycle orchestration and operations-aware metering
How The Backbone Deepens
Unified Data Model & Traceability
It expands from datasets and episodes into training runs, promoted models, artifacts, deployments, telemetry, interventions, and financial accountability.
Orchestration
It starts with data-to-dataset automation and grows into promotion, rollout, rollback, maintenance, and human intervention workflows.
Governance & Tenancy
It deepens from workspace isolation into region policy, environment promotion rules, operational approvals, and regulated deployment controls.
Metering & Billing
It becomes more valuable as the platform adds storage, compute, media processing, rollout, and fleet operations that need cost visibility.
Why The Sequence Works
It follows real pain
The first wedge targets the data-to-training bottleneck where robotics teams already feel the infrastructure burden most acutely.
It preserves one architecture
Each phase deepens the same backbone and component map instead of inventing a new platform taxonomy at every stage.
It compounds adoption
Every additional phase enriches the same system of record, which makes the already-adopted parts of the platform more valuable.
It keeps roadmap promises grounded
Expansion follows proven usage and deeper trust, not speculative feature sprawl disconnected from the wedge that wins first.