AI Buyer Insights:

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

Swedbank, a Temenos T24 customer evaluated Oracle Flexcube

Wayfair, a Korber HighJump WMS customer just evaluated Manhattan WMS

Michelin, an e2open customer evaluated Oracle Transportation Management

Moog, an UKG AutoTime customer evaluated Workday Time and Attendance

Cantor Fitzgerald, a Kyriba Treasury customer evaluated GTreasury

Citigroup, a VestmarkONE customer evaluated BlackRock Aladdin Wealth

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

Swedbank, a Temenos T24 customer evaluated Oracle Flexcube

Wayfair, a Korber HighJump WMS customer just evaluated Manhattan WMS

Michelin, an e2open customer evaluated Oracle Transportation Management

Moog, an UKG AutoTime customer evaluated Workday Time and Attendance

Cantor Fitzgerald, a Kyriba Treasury customer evaluated GTreasury

Citigroup, a VestmarkONE customer evaluated BlackRock Aladdin Wealth

Virgin Hyperloop Data, Technology Stack, and Enterprise 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
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.
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
Collaboration Collaboration 2020 2020
Event Management Collaboration 2016 2016
Content Management
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Digital Signing Content Management 2022 2022
Enterprise Content Management Content Management 2020 2020
Web Content Management Content Management 2020 2020
Web Content Management Content Management 2020 2020
CRM
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Marketing Automation CRM 2020 2020
Procurement
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Procurement Procurement 2018 2018
Supplier Relationship Management Procurement 2018 2018
PaaS
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Apps Development PaaS 2021 2021
Transactional Email PaaS 2020 2020
IaaS
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Application Hosting and Computing Services IaaS 2017 2017
Application Hosting and Computing Services IaaS 2020 2020
Content Delivery Network IaaS 2021 2021
IT Decision Makers and Key Stakeholders at Virgin Hyperloop
First Name Last Name Title Function Department Email Phone
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Apps Being Evaluated by Virgin Hyperloop Executives
Date Company Status Vendor Product Category Market
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FAQ - APPS RUN THE WORLD Virgin Hyperloop Technographics

Virgin Hyperloop is a Transportation organization based in United States, with around 500 employees and annual revenues of $153.0 million.

Virgin Hyperloop operates a diverse technology stack with applications such as Greenhouse ATS, Databricks MLflow and Zoom, covering areas like Applicant Tracking System, ML and Data Science Platforms and Audio Video and Web Conferencing.

Virgin Hyperloop has invested in cloud applications and AI-driven platforms to optimize efficiency and growth, collaborating with vendors such as Greenhouse, Databricks and Zoom Video Communications.

Virgin Hyperloop recently adopted applications including DocuSign eSignature in 2022, Zoom in 2021 and Salesforce Heroku in 2021, highlighting its ongoing modernization strategy.

APPS RUN THE WORLD maintains an up-to-date database of Virgin Hyperloop’s key decision makers and IT executives, available to Premium subscribers.

Our research team continuously updates Virgin Hyperloop’s profile with verified software purchases, vendor relationships, and digital initiatives identified from public and proprietary sources.

Subscribe to APPS RUN THE WORLD to access the complete Virgin Hyperloop technographics profile, including detailed breakdowns by category, vendor, and IT decision makers.