Bootstrap a CDF project with a production-ready foundation: infrastructure setup, data modeling, ingestion, contextualization, and quality reporting in one curated set.
Quickstart Enterprise Data Model
Common Data Model (CDM) with contextualization workflows for industrial environments. Covers data modelling fundamentals and automated linking via Cognite Toolkit.
Reliability & Maintenance Data Model
ISO 14224 / NORSOK Z-008 compliant containers and views for assets, equipment, maintenance orders, and failure analysis. Prerequisite for RCA packs.
A tag-centric data model for oil and gas operations that merges data from AVEVA, SAP, OPC UA, and PI into a single queryable structure. Built on the CFIHOS 2.0 standard.
ISA-95 / ISA-88 compliant manufacturing model. Includes spaces, containers, views, SQL transformations, orchestration workflows, and optional RAW seed data.
Automates linking P&ID documents to assets and related files via CDF Data Modeling. Replaces manual annotation with automated workflows for improved traceability.
Production-ready solution to link time series, 3D nodes, and entities to assets/tags using CDF Data Modeling. Reduces manual mapping with high-precision automation.
Atlas AI Property Extractor
Bridges unstructured documents and structured CDF data using Atlas AI agents. Extracts equipment tags, metadata, and relationships from engineering reports and technical manuals.
Fast-tracks InField implementation with pre-configured data model, turnkey configuration storage, integrations, and visualizations. Minimal setup to get field teams productive.
Centralized visualization of contextualization health across asset hierarchies, time series, 3D models, maintenance workflows, and file annotations in a single view.
CDF Project Health Dashboard
Monitors extraction pipelines, workflows, transformations, and functions scoped by dataset. Streamlit dashboard with health gauges, status donuts, and recent error tables.
Web app for troubleshooting CDF deployment quality — diagnosing search result discrepancies, identifying failing activities, and reducing processing bottlenecks.
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