Los Angeles, 90021, CA,
United States
Virgin Hyperloop Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by Virgin Hyperloop and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 500 Virgin Hyperloop employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that Virgin Hyperloop has purchased the following applications: Greenhouse ATS for Applicant Tracking System in 2020, Databricks MLflow for ML and Data Science Platforms in 2020, Zoom for Audio Video and Web Conferencing in 2021 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems Virgin Hyperloop is running and its propensity to invest more and deepen its relationship with Greenhouse , Databricks , Zoom Video Communications or identify new suppliers as part of their overall Digital and IT transformation projects to stay competitive, fend off threats from disruptive forces, or comply with internal mandates to improve overall enterprise efficiency.
We have been analyzing Virgin Hyperloop revenues, which have grown to $153.0 million in 2024, plus its IT budget and roadmap, cloud software purchases, aggregating massive amounts of data points that form the basis of our forecast assumptions for Virgin Hyperloop intention to invest in emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database or in cloud-based ERP, HCM, CRM, EPM, Procurement or Treasury applications.
HCM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Greenhouse | Legacy | Greenhouse ATS | Applicant Tracking System | HCM | n/a | 2020 | 2020 |
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AI Development
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Databricks | Legacy | Databricks MLflow | ML and Data Science Platforms | AI Development | n/a | 2020 | 2020 |
In 2020, Virgin Hyperloop deployed Databricks MLflow on Databricks as part of its ML and Data Science Platforms strategy to support analytics and machine learning workloads for simulation and demand forecasting. The deployment centralized big data processing and experiment tracking for the Hyperloop data team, supporting scenario runs that predict passenger demand by hour, by day, and by specific origins and destinations.
The implementation leveraged Databricks runtime capabilities together with Koalas to scale pandas workloads with minimal code changes, enabling the team to port existing pandas code into distributed Spark execution. Using Koalas to scale pandas workflows reduced data processing time by as much as 95 percent, accelerating both batch processing and exploratory analytics used in simulation experiments.
Databricks MLflow was employed specifically for MLflow Tracking to log, version, and visualize simulation outputs, with each simulation treated as an experiment. Databricks MLflow standardized experiment metadata, model artifacts, and run metrics so simulation outputs for safety assessment and demand forecasting could be compared, audited, and reviewed across runs.
Operational scope focused on the data engineering and analytics functions within Virgin Hyperloop, where scenario modeling directly informed operational planning such as vehicle scheduling. The rollout converted ad hoc simulation tracking into a governed experiment tracking workflow, reducing internal tooling development and creating a repeatable process to validate simulation assumptions and outputs.
Explicit outcomes from the engagement include the reported 95 percent reduction in data processing time and simulation driven planning that reduced the number of operating vehicles by 70 percent in modeled scenarios. Virgin Hyperloop also documented significant development time savings by not building a bespoke simulation tracking tool, positioning Databricks MLflow as the central experiment management capability.
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Collaboration
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Zoom Video Communications | Legacy | Zoom | Audio Video and Web Conferencing | Collaboration | n/a | 2021 | 2021 |
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Collaboration | Collaboration |
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2020 | 2020 |
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Event Management | Collaboration |
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2016 | 2016 |
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Content Management
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Digital Signing | Content Management |
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2022 | 2022 |
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Enterprise Content Management | Content Management |
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2020 | 2020 |
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Web Content Management | Content Management |
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2020 | 2020 |
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Web Content Management | Content Management |
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2020 | 2020 |
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CRM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Marketing Automation | CRM |
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2020 | 2020 |
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Procurement
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Procurement | Procurement |
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2018 | 2018 |
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Supplier Relationship Management | Procurement |
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2018 | 2018 |
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PaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Apps Development | PaaS |
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2021 | 2021 |
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Transactional Email | PaaS |
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2020 | 2020 |
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IaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Application Hosting and Computing Services | IaaS |
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2017 | 2017 |
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Application Hosting and Computing Services | IaaS |
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2020 | 2020 |
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Content Delivery Network | IaaS |
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2021 | 2021 |
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