Use Cases

Delta Bravo AI Use Cases for Financial Services, Manufacturing, Public Infrastructure and More. Delta Bravo AI Use Cases for Financial Services, Manufacturing, Public Infrastructure and More. Delta Bravo AI Use Cases for Financial Services, Manufacturing, Public Infrastructure and More.Delta Bravo AI Use Cases for Financial Services, Manufacturing, Public Infrastructure and More.Delta Bravo AI Use Cases


Our customers range from companies just starting their data journey to advanced enterprises with complex processes and data science teams. We give them a crystal-clear understanding of what they can do with the data they have today as well as the steps needed to reach the next level tomorrow. 

Our process starts with a focused Business Case discussion with the goal of aligning data to critical process areas and tracking the ROI of the AI effort. We collect sample datasets and run them through the Delta Bravo platform to determine whether or not we have the data necessary to solve the business problem. We evaluate the scalability of current systems and their ability to support AI operations. We share our analysis of any gaps, trends, correlations and additional use cases that could be achieved with the dataset. We construct our approach, scope and ROI model and deliver a plan for deployment.

What To Expect:

  • 3-5 week project, with 3-4 meetings between 30-60 minutes in length
  • Candid feedback on state of data quality, collection and analysis processes
  • Polished deliverable analyzing current state opportunities and future roadmap for data value

The Delta Bravo AI Readiness Deliverable includes:

  • Data Quality and Quantity Assessment
  • Data Relevance Assessment
  • Visualization and Analysis for Trends, Correlations in Delta Bravo Platform
  • Preliminary Modeling and Capability Identification: Use Cases and ROI
  • AI Capability Operationalization and Deployment Plan
  • Infrastructure and Network Scalability Analysis
  • Cost Analysis and ROI Model

We help companies identify valuable data and build the backend infrastructure for collecting, storing and scaling its value. Using Kubernetes optimizes our footprint and keeps costs low as AI capabilities are scaled out across multiple departments and locations. Speed, scalability and sensibility matter- our objective is to make your team more efficient and effective without giving them “more to do.”

Services include:


  • Data Lake Architecture and Deployment
  • Machine Learning environment setup and ongoing management; expertise in AWS, Microsoft Azure, Cisco, Diamanti, Kubernetes, Kubeflow and more
  • Developing custom collection mechanisms to move data from sensors, machines, and other sources to a central repository for visualization and analysis
  • Database upgrades and migrations

Delta Bravo’s diverse Data Science team brings experience and perspective across different industries and modeling methods. We are creative, focused and detailed in process and approach. Some customers use our team as their own outsourced Data Science group. Others with existing Data Science teams use us for a fresh perspective or to fill gaps and keep pace with project demand. We’ve executed Supervised, Semi-Supervised and Unsupervised Machine Learning methods in Manufacturing, Financial Services, Retail and Healthcare.

Services include:

  • Data Cleansing and Joining
  • Data Visualization and Trend Analysis
  • Machine Learning model creation
  • Machine Learning Functional Pilot Development
  • Machine Learning Production Application Development


Our team quickly deploys sensible, scalable and easy-to-use AI solutions. However, they are not “set and forget.” Models need to be tuned, retrained and audited for security. We offer services to handle these tasks ongoing, leveraging our Data Science and Development teams to ensure your models and systems are operating at peak efficiency and performance levels throughout your AI journey.

Services include:


  • Management of Data Science infrastructure and environment (performance, efficiency, security)
  • Ongoing tuning and retraining of Machine Learning models
  • Ongoing evaluation of new and/or better models that could potentially apply to your use case
  • Management of interface (application or API) used to deliver Model to users