AI Buyer Insights:

Michelin, an e2open customer evaluated Oracle Transportation Management

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

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

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

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

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

Swedbank, a Temenos T24 customer evaluated Oracle Flexcube

Wayfair, a Korber HighJump WMS customer just evaluated Manhattan WMS

Springboard Data, Technology Stack, and Enterprise Applications
ERP Financial Management
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Stripe Legacy Stripe Payments Payment Processing ERP Financial Management n/a 2020 2020
HCM
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Employ Legacy Lever by Employ Applicant Tracking System HCM n/a 2021 2021
Applicant Tracking System HCM 2021 2021
Interview Scheduling HCM 2021 2021
AI Development
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Google Legacy Google BigQuery ML ML and Data Science Platforms AI Development n/a 2018 2018
In 2018, Springboard implemented Google BigQuery ML to perform predictive analysis on the Stack Overflow real-world dataset, deploying the solution as part of its ML and analytics capability set. Google BigQuery ML was used to run in-database model training and predictive queries directly against the Stack Overflow dataset, keeping data and model artifacts inside the BigQuery environment. The implementation leveraged Google BigQuery ML functionality for SQL-based model creation, iterative model evaluation, and in-place feature engineering, consistent with ML and Data Science Platforms workflows. Teams used BigQuery ML model types and evaluation routines to prototype classification and regression experiments, and they persisted models in BigQuery for repeatable scoring and batch prediction. Operationally the work centered on the data science and analytics organization, with the Stack Overflow real-world dataset ingested into Google BigQuery as the primary data source. Data ingestion and transformation workflows were implemented as SQL pipelines inside BigQuery, enabling analysts to run predictive analysis without moving large volumes of data out of the platform. Governance was structured around BigQuery dataset access controls and SQL-based model lifecycle practices, enabling controlled experimentation and model versioning within the Google Cloud environment. The narrative emphasizes Springboard Google BigQuery ML implementation within the ML and Data Science Platforms category, and highlights architecture choices that kept modeling, feature engineering, and prediction tightly coupled to the BigQuery data warehouse.
AI-Powered Application
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Chatbots and Conversational AI AI-Powered Application 2020 2020
Collaboration
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Collaboration Collaboration 2020 2020
Collaboration Collaboration 2011 2011
Survey and Questionnaire Collaboration 2018 2018
Content Management
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Digital Signing Content Management 2022 2022
CRM
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
CRM CRM 2018 2018
Customer Data Platform CRM 2018 2018
Customer Experience CRM 2018 2018
Customer Support CRM 2020 2020
Digital Advertising Platform CRM 2015 2015
Digital Advertising Platform CRM 2017 2017
Digital Advertising Platform CRM 2020 2020
Marketing Analytics CRM 2015 2015
Marketing Analytics CRM 2015 2015
Marketing Analytics CRM 2016 2016
Marketing Analytics CRM 2016 2016
Marketing Analytics CRM 2018 2018
Marketing Automation CRM 2016 2016
Marketing Automation CRM 2021 2021
Marketing Automation CRM 2020 2020
Marketing Automation CRM 2020 2020
Sales Automation, CRM, Sales Engagement CRM 2021 2021
ITSM
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
IT Service Management ITSM 2020 2020
PaaS
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
API Management, iPaaS (Integration Platform as a Service) PaaS 2022 2022
Transactional Email PaaS 2015 2015
Transactional Email PaaS 2015 2015
Transactional Email PaaS 2020 2020
IaaS
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Application Hosting and Computing Services IaaS 2012 2012
Content Delivery Network IaaS 2020 2020
Content Delivery Network IaaS 2015 2015
IT Decision Makers and Key Stakeholders at Springboard
First Name Last Name Title Function Department Email Phone
No data found
Apps Being Evaluated by Springboard Executives
Date Company Status Vendor Product Category Market
No data found
FAQ - APPS RUN THE WORLD Springboard Technographics

Springboard is a Professional Services organization based in United States, with around 3000 employees and annual revenues of $600.0 million.

Springboard operates a diverse technology stack with applications such as Stripe Payments, Lever by Employ and Google BigQuery ML, covering areas like Payment Processing, Applicant Tracking System and ML and Data Science Platforms.

Springboard has invested in cloud applications and AI-driven platforms to optimize efficiency and growth, collaborating with vendors such as Stripe, Employ and Google.

Springboard recently adopted applications including DocuSign eSignature in 2022, Zapier Automation Platform in 2022 and Lever by Employ in 2021, highlighting its ongoing modernization strategy.

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

Our research team continuously updates Springboard’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 Springboard technographics profile, including detailed breakdowns by category, vendor, and IT decision makers.