Delta Bravo is built to process massive amounts of time-series data and generate detailed trend analysis, predictions and recommendations. 

Delta Bravo, AI For the DatabaseFor every advantage data creates, there is an equally impactful cost and risk. Whether its on-premise or in the cloud, data has to live somewhere and that costs money. Data growth is spurred by integration with outside data sources and devices create security and compliance risks that have taken down global brands. Mission critical applications struggle to meet data demands, slowing performance and impacting customer satisfaction.

IT departments are overwhelmed and help is not on the way. A leading jobs site just published a survey showing that for every 100 jobs they post for data professionals, there are 14 applicants.


Delta Bravo is an ideal solution for companies seeking to grow with the same amount of people (or less) than they have today. Delta Bravo uses Artificial Intelligence to handle the time-consuming work of database management, such as deep correlation analysis, capacity planning, workload optimization and database security.

Delta Bravo helps technical resources resolve data performance issues 90% faster than conventional tools. Delta Bravo handles database security and compliance checks- manual tasks that eat up time if they are done at all. Delta Bravo is the faithful assistant who does the grunt work, filtering out the noise to provide the information you need to make fast, accurate decisions.


We use a highly scalable and repeatable framework for modeling, learning and forecasting.

Delta Bravo, AI for the Database





Delta Bravo’s AI model collects Training Data from three sources. First, we collect Operational Counters from the targeted database technology (SQL Server, MySQL or PostgreSQL) and Operating System (Windows or Linux). The customer installs our Data Collector in a VM behind their firewall. The Data Collector is agentless and collects operational counters in a time-series format. This process is efficient, secure and has no impact on performance.

The second source is the user’s own maintenance patterns. We track changes in the environment, measure their impact and build context for what kinds of maintenance occur most often and have the most impact. The third source is through Curated Operational Indicators, in-application dialogue prompts that ask the user contextual questions about the application.

The combination of these three inputs with our proprietary correlation algorithms and machine learning models lay the foundation for Delta Bravo to understand and predict your database maintenance, performance and security best practices.


Delta Bravo receives the encrypted operational counters and processes them through the Analysis Engine. Operational counters are measured against Best Practices and run through a series of correlation algorithms. The correlation algorithms are used to identify the root cause of performance issues, segmenting them into Cause Groups. Cause Groups include Database Maintenance, Queries, I/O, CPU and Memory.

Algorithm results are expressed through Recommendations. Recommendations include a brief overview of what factors were evaluated to determine the root cause. The operational counters used in the algorithm are visualized so the user can see the correlation and when the condition occurred. Delta Bravo summarizes the issue’s impact on User Experience and Cost, then provides a Recommended Fix based on Best Practices. When applicable, Delta Bravo will also provide any code necessary to solve the problem.


Machine learning is used to help Analysis and Recommendations become more individualized to each database instance’s use case over time. For example, Delta Bravo Performance scores are based on over 100 Best Practice checks. Each check starts with a unique “weight” that helps the Delta Bravo model prioritize each recommendation’s impact. Over time, each Best Practice performance check’s weight changes in accordance to its impact on your environment. Therefore, Delta Bravo will recommend different resolution tactics for each database instance, depending on how its used and the impact of the change based on context.


While AI for Database Management is a valuable and highly complex first step, we’re just getting started.

The strength of the Delta Bravo AI Platform is its ability to scale, collate, correlate and analyze massive amounts of data. Our inputs and models are flexible, so nearly any combination of time-series datapoints can be used in our solution. For example, leveraging historical and forecasted weather data against call center volumes to predict operational staffing guidelines and logistics costs.

Ideas like this are just the tip of the iceberg for the Delta Bravo platform. Stick with us- we appreciate your attention and support in being part of our growth.