The Delta Bravo platform reduces the time and cost to deploy Machine Learning and Artificial Intelligence capabilities.

Industry leaders like Rolls-Royce, Toyota, Atlas Copco, AgFirst, JTEKT, Elkem Silicones and AccuWeather use Delta Bravo’s technology and service teams to deliver Machine Learning and AI solutions that produce millions of dollars in savings across multiple lines of business.


Gain greater impact in Overall Equipment Efficiency, Increased Throughput, Reduced Cost of Poor Quality and improved Supply Chain efficiency.

Reduce Cost, Unplanned Downtime with Predictive Maintenance


Delta Bravo leverages data and context to generate more accurate predictions of the lifespan for a component given environmental conditions. When specific failure signals are observed, or component aging criteria is projected, Delta Bravo forecasts this to manufacturing operations, giving them several weeks advanced notice of when components should be replaced.

Case Study:
Delta Bravo is currently working with a global manufacturer with over 4,000 machines in 250 client sites worldwide. We’re helping the manufacturer forecast maintenance events and proactively train client operators to optimize planned downtime and reduce costly stoppage. The manufacturer anticipates a 20% reduction in internal maintenance costs, up to a 75% downtime reduction for customers and 25% longer life of parts through better trained operators.

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Higher First Pass Yield, Lower Cost of Poor Quality (COPQ)


From assemblers to component manufacturers to plastics and additives, Delta Bravo helps teams identify anomalies and hard-to-find factors influencing production output. We work with QA, Operations and R&D teams to leverage data that’s being collected today and deploy trained models in a way that supports, not disrupts the business.

Case Study:
Delta Bravo is currently working with Rolls Royce to predict engine failures on a test bed. Our models integrate with existing Operator software to stop the test prior to failure, provide a Root Cause and enable the Operator to restart the test; creating a new capability without disrupting existing processes or requiring training on new systems.

Delta Bravo is also working with a major plastics manufacturer, assisting their R&D team in identifying anomalies that impact varying outputs from the same recipe. Our correlation models helped the team identify minute changes reducing quality issues by 67%, saving the company months of time and millions of dollars.

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Increased Throughput, Targeted Scrap Reduction


Increased Throughput
Delta Bravo models are in place with a global tape manufacturer, correlating data from machine-level loads and production schedules to increase throughput by over 20%. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms.

Targeted Scrap Reduction
Delta Bravo recently completed an engagement that traced the root cause of component waste to a particular machine process. A machine learning model was applied to this process, enabling the manufacturer to predict if the process would have an 85% or better chance of success. If lower than 85%, the process was terminated and restarted. This reduced scrap rates, saving the manufacturer tens of thousands of dollars per month in wasted materials and improved time-to-production metrics.

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Supply Chain Optimization, Demand and Inventory Forecasting


Getting the Right Part to the Right Place at the Right Time
Delta Bravo worked with a Top 20 Global Logistics company to deploy models that predict route, driver and truck configuration efficiency.  These models would score each of these factors and provide recommendations for increasing efficiency scores through route adjustments, driver assignment and optimized shipping configurations.  These recommendations saved the customer nearly $4M in the first year.

Predicting Inventory Movement
Delta Bravo worked with an industry leader in the tire field to improve upon their existing inventory movement forecasting models, improving them by over 60%, saving them nearly $5M annually.  We leveraged data in their existing models, but also added weather data and scraped the web for competitor specials that influenced their sales and demand.

Demand Forecasting Across the Supply Chain
Delta Bravo worked with a leading beverage manufacturer in the Walmart ecosystem to predict demand in new stores in new markets. The product had a 24-day shelf life and quantities needed to be optimized to avoid waste. Delta Bravo’s models proved 10% more efficient than previous attempts.


Know Your Customer.  Reduce Fraud and Operational Cost. Increase Revenue.

delta bravo, machine learning, AI for the databaseDelta Bravo AI empowers administrators to be proactive if transactions start to spike above or fall below the norm so they can take action before an outage in service or a fraud scenario. Delta Bravo evaluates expected data volumes based on historical patterns and applies boundaries based on volume variation. This system is then used to compare real-time transaction value to expected volume, identifying anomalies in activity, resource consumption and more.

Within seconds, Delta Bravo Database identifies compliance gaps across the entire data tier, prioritizes them and provides the step-by-step instructions and code for the fix. Financial firms manage an ever-growing amount of data, coming in from trading systems, mobile applications, reporting platforms and more. IT Ops and Compliance teams are turning what used to take months into resolution in minutes. This solution is applicable to SOX, GLB, GDPR and more.

Detecting and preventing fraud is a huge challenge for banks given the large variety of fraud types and the volume of transactions that need to be reviewed and manual or rules-based systems can’t keep up. Delta Bravo AI analyzes transactions and looks for indicators of suspicious behavior including transactions with dubious jurisdictions, suspicious companies or known parties. If fraud patterns are detected, Delta Bravo AI triggers processes to reject transactions outright or flag transactions for investigation and can even score the likelihood of fraud, so investigators can prioritize their work on the most promising cases.

Delta Bravo AI helps process and analyze mountains of customer data, segmenting customers in easy-to-understand groups. From there, we’re able to personalize content and offers built to meet the expectations and needs of each segment, increasing revenue and customer satisfaction. Our models can integrate with email marketing, SMS and other outreach mechanisms as needed.


Delta Bravo AI identifies credit payment and churn risks, leveraging various data sources to score applicants. Scoring and risk analysis is based on a number of factors like current income, employment opportunity, recent credit history, and ability to earn in addition to older credit history. Patters are also able to detect credit card “churners,” applicants who are rarely profitable for the card issuer. Delta Bravo AI also provides reason codes for credit decisions that explain the key factors in credit decisions, meeting compliance requirements for GLB.


Know Your Customer.  Predict Traffic, Sales and Staffing Volumes. Personalize Offers and Increase Revenue.

Correlating Data From Multiple Sources

Most retail businesses have several data sources: point of sale systems, E-commerce, PPC, email marketing, SMS, even systems like Cisco DNA Spaces and Meraki. Pulling this data together and correlating it to better understand customer behavior and operational insights can be difficult.  Delta Bravo has a proven history of delivering insight and value to retail leaders.

Case Study:
Delta Bravo has worked with AccuWeather to integrate weather data with retail sales and staffing data to optimize patterns driven by the weather.  Companies like Starbucks, UPS and more leverage this capability to optimize operational expenses and personalize specials.

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Demand Forecasting, Customer Segmentation and Purchase Correlation


Predicting sales volume, inventory movement and more are among the successful use cases Delta Bravo has executed in the Retail Space.

Case Study:
Delta Bravo is currently working with a major US retail and restaurant group, ingesting data from several different sources to create customer segments by geography and brand. We are correlating items that are purchased together with particular segments to better personalize offers and manage inventory.  Our models integrate with their customer outreach systems via API and forecasting scenarios are visualized in their existing analytics software.


Actionable predictions and recommendations that improve operations, safety and citizen experiences.

Anticipate Disaster and Improve Response

Most retail businesses have several data sources: point of sale systems, E-commerce, PPC, email marketing, SMS, even systems like Cisco DNA Spaces and Meraki. Pulling this data together and correlating it to better understand customer behavior and operational insights can be difficult.  Delta Bravo has a proven history of delivering insight and value to retail leaders.

Case Study: Optimize Emergency Reponse and Evacuation
Delta Bravo makes public infrastructure dynamically responsive to emergency scenarios. We are working with a city leveraging forecasted flood plain patterns to adjust traffic signals for efficient evacuation of civilians and faster entry for emergency response teams. Delta Bravo can also compare and optimize human developed courses of action (COAs) alongside computer generated courses of action including the criteria for suitability, feasibility, acceptability, uniqueness and completeness.

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Improve Precision and Efficiency in Deployment of Emergency Responders



Case Study:
The City of Rock Hill, SC is adjacent to fastest growing town in USA (Fort Mill, SC). The area’s growth has made it difficult to know where to place limited police and emergency responders. Delta Bravo ingested historic traffic accident data, traffic light data, weather data, construction permit data and city event data to build a model predicting traffic accidents with over 80% accuracy down to the intersection. We developed an alert system to help deploy city resources to right place at right time.