List of Cemit AI Data Platform Customers
Skien, 3717,
Norway
Since 2010, our global team of researchers has been studying Cemit AI Data Platform customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Cemit AI Data Platform for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Cemit AI Data Platform for ML and Data Science Platforms include: CargoNet Norway, a Norway based Transportation organisation with 402 employees and revenues of $120.0 million, Bybanen Norway, a Norway based Transportation organisation with 47 employees and revenues of $33.0 million, Train Alliance Sweden, a Sweden based Manufacturing organisation with 10 employees and revenues of $15.0 million and many others.
Contact us if you need a completed and verified list of companies using Cemit AI Data Platform, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Bybanen Norway | Transportation | 47 | $33M | Norway | New Normal Group | Cemit AI Data Platform | ML and Data Science Platforms | 2023 | n/a |
In 2023, Bybanen Norway deployed the Cemit AI Data Platform to support operations and infrastructure monitoring for the Bergen Light Rail network in Norway. The implementation positions the Cemit AI Data Platform within the ML and Data Science Platforms category to ingest operational telemetry and surface analytics for ride comfort and asset condition monitoring.
Configuration centered on two primary capability areas inferred from CEMIT product focus and referenced case mentions, ride-quality monitoring and predictive maintenance. The Cemit AI Data Platform configuration is consistent with ML and Data Science Platforms patterns, including time series ingestion from sensor streams, feature engineering and model training pipelines, anomaly detection and scoring, and dashboarding for operational users. Module usage such as ride-quality monitoring and predictive maintenance is inferred from CEMIT's product focus and referenced case mentions rather than an explicit module-level case study.
Operational scope emphasized operations and infrastructure maintenance teams in Norway, with outputs intended to detect ride comfort degradations and anticipate equipment failures to improve service reliability as explicitly expected in source notes. Governance and operational workflows are expected to include model lifecycle management, alerting and escalation paths into maintenance planning, and centralized monitoring of model performance to support ongoing operations, consistent with the role of ML and Data Science Platforms in transit infrastructure monitoring.
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CargoNet Norway | Transportation | 402 | $120M | Norway | New Normal Group | Cemit AI Data Platform | ML and Data Science Platforms | 2021 | n/a |
In 2021, CargoNet Norway implemented the Cemit AI Data Platform. Cemit AI Data Platform is positioned as an ML and Data Science Platforms solution used for freight rail operational analytics and for asset and route monitoring across Norway.
The implementation scope centered on freight rail operational analytics, with inferred module-level roles aligned to Cemit product descriptions such as track and asset monitoring, time series analytics, anomaly detection, and operational reporting. The Cemit AI Data Platform was configured to support data ingestion pipelines, model training and serving workflows, and operational dashboards, enabling model orchestration and feature engineering consistent with ML and Data Science Platforms capabilities.
Integrations emphasized telemetry and operational feed consolidation, including onboard rolling stock sensors, wayside monitoring data, scheduling and dispatch logs, and asset registries, to create unified datasets for routing and utilization analysis. The platform architecture as deployed for CargoNet Norway prioritized continuous data ingestion, time series storage and analytics, and near real time alerting to surface disruptions and asset utilization patterns.
Operational governance focused on data access controls, model validation and versioning, and staged rollout to operations and network planning teams across CargoNet Norway. The deployment was intended to reduce service disruptions and improve asset utilisation by enabling analytics driven monitoring and operational decision support using the Cemit AI Data Platform.
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Train Alliance Sweden | Manufacturing | 10 | $15M | Sweden | New Normal Group | Cemit AI Data Platform | ML and Data Science Platforms | 2022 | n/a |
In 2022, Train Alliance Sweden implemented Cemit AI Data Platform as its ML and Data Science Platforms choice to support rail operations and maintenance analytics. The deployment is described for use across its facilities in the Nordic region, with an explicit operational objective to optimise uptime and maintenance planning for rolling stock and depot operations.
Implementation signals indicate the deployment centers on predictive maintenance and operational analytics capabilities aligned with CEMIT's railway intelligence offering, with data ingestion, feature engineering, model training and inference workflows playing central roles. The Cemit AI Data Platform is presented as the primary analytical layer for condition based monitoring, time series analysis and anomaly detection use cases, reflecting typical functional modules in ML and Data Science Platforms.
Reported scope covers operational and maintenance functions within Train Alliance Sweden, suggesting adoption by maintenance planners and operations teams across regional sites. The engagement is described with an implied emphasis on data governance and model validation workflows to operationalize predictions for scheduling and planning, consistent with standard practices for industrial ML deployments.
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