Denver, 80216, CO,
United States
Brandfolder Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by Brandfolder and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 200 Brandfolder employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that Brandfolder has purchased the following applications: Databricks MLflow for ML and Data Science Platforms in 2018, TeamSupport SnapEngage for Chatbots and Conversational AI in 2013, Google Workspace (Formerly Google G-Suite) for Collaboration in 2013 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems Brandfolder is running and its propensity to invest more and deepen its relationship with Databricks , TeamSupport , Drift 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 Brandfolder revenues, which have grown to $13.0 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 Brandfolder 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 |
|---|---|---|---|---|---|---|---|---|
| Databricks | Legacy | Databricks MLflow | ML and Data Science Platforms | AI Development | n/a | 2018 | 2018 |
In 2018 Brandfolder implemented Databricks MLflow as a core component of an ML platform to enable a data driven creative experience for its customers, integrating the application into a small team, constrained budget delivery model. This implementation situates Databricks MLflow within the ML and Data Science Platforms category, supporting experiment tracking, model versioning and both batch and real time serving workflows for product facing use cases.
The technical implementation used Spark based ETL to prune transactional events and store partitioned parquet files in Google Cloud Storage, forming a centralized data lake. Feature extraction was implemented with custom Spark user defined functions and persisted to a global feature store on GCS, while MLLib libraries were used to prototype and build initial models. Databricks MLflow was configured as the experiment and artifact registry, logging model parameters, metrics and artifacts to a dedicated mlflow server endpoint.
Operational architecture integrated cloud data processing on Dataproc with a Kubernetes hosted mlflow server, enabling model serving and selection of best performing experiments. Scoring paths were implemented in both batch pipelines and low latency real time flows, with each ML use case exposed via a gRPC ML service that invokes the feature UDFs, calls the model served from mlflow and returns scored results to the calling client. The deployment combines data lake storage, Spark based feature engineering, MLLib model development and Databricks MLflow driven model lifecycle management.
Governance centered on centralized experiment tracking and model versioning through Databricks MLflow, which acted as the single source for model artifacts used in production scoring. Rollout focused on embedding ML services into product workflows, with development and serving separated across Kubernetes for model endpoints and Dataproc for data processing, enabling controlled promotion of experiments to production. The narrative reflects an implementation that links the Databricks MLflow application to product engineering and data teams under the ML and Data Science Platforms function.
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AI-Powered Application
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| TeamSupport | Legacy | TeamSupport SnapEngage | Chatbots and Conversational AI | AI-Powered Application | n/a | 2013 | 2013 |
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Chatbots and Conversational AI | AI-Powered Application |
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2018 | 2018 |
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Chatbots and Conversational AI | AI-Powered Application |
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2020 | 2020 |
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Collaboration
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Legacy | Google Workspace (Formerly Google G-Suite) | Collaboration | Collaboration | n/a | 2013 | 2013 |
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Content Management
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Digital Asset Management | Content Management |
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2017 | 2017 |
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CRM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Account Based Marketing | CRM |
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2023 | 2023 |
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Account Based Marketing | CRM |
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2020 | 2020 |
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Account Based Marketing, Sales Engagement | CRM |
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2019 | 2019 |
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Customer Support | CRM |
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2017 | 2017 |
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Marketing Analytics | CRM |
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2015 | 2015 |
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Marketing Analytics | CRM |
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2017 | 2017 |
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Marketing Analytics | CRM |
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2021 | 2021 |
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Marketing Automation | CRM |
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2015 | 2015 |
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Marketing Automation | CRM |
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2018 | 2018 |
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Marketing Automation | CRM |
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2019 | 2019 |
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Marketing Automation | CRM |
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2020 | 2020 |
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Marketing Automation, Sales Automation | CRM |
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2021 | 2021 |
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Sales Automation, CRM, Sales Engagement | CRM |
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2021 | 2021 |
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PPM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Project Portfolio Management | PPM |
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2020 | 2020 |
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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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2016 | 2016 |
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Transactional Email | PaaS |
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2013 | 2013 |
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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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2015 | 2015 |
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Application Hosting and Computing Services | IaaS |
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2021 | 2021 |
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Application Hosting and Computing Services | IaaS |
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2021 | 2021 |
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Content Delivery Network | IaaS |
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2019 | 2019 |
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Content Delivery Network | IaaS |
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2021 | 2021 |
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