Rize Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by Rize and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 130 Rize employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that Rize has purchased the following applications: MindsDB for ML and Data Science Platforms in 2023 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems Rize is running and its propensity to invest more and deepen its relationship with MindsDB 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 Rize revenues, which have grown to $25.0 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 Rize 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.
AI Development
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| MindsDB | Legacy | MindsDB | ML and Data Science Platforms | AI Development | n/a | 2023 | 2023 |
In 2023, Rize implemented MindsDB as part of its ML and Data Science Platforms work to add ML-driven product features and retention insights. The deployment used MindsDB Cloud to train and auto-deploy models directly from Rize's primary product database, with a focus on product and engagement analytics in the United States.
The implementation leveraged MindsDB Cloud's train-and-deploy capabilities to create model pipelines that surface engagement signals and retention predictors into the product stack. Functional configuration included automated model training, scheduled retraining workflows, and deployed inference endpoints for scoring, with pipelines orchestrated from the source database through MindsDB Cloud to the application layer.
Operational coverage centered on product analytics and customer retention use cases affecting product and analytics teams, and rollout governance emphasized embedding model deployment into the engineering workflow to accelerate feature delivery. According to Rize's CTO the approach substantially shortened development time and saved many thousands of dollars, outcomes attributed to MindsDB Cloud's direct train-and-deploy model path.
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