Company attributes
DataCrunch Lab, LLC was cofounded by Zeydy Ortiz and Rob Montalvo in 2015. The company focuses on facilitating the adoption of artificial intelligence and machine learning in business and government using open-source and commercial software and cloud computing resources.
The company's analytics offering utilizes modeling functions to measure and monitor risk metrics related to physical or financial energy assets and contracts. Uses of DataCrunch Lab's analytics include the following:
- Data mining and big data predictive analytics
- Statistical analysis for accuracy in answers to questions
- Forecasting and planning
- Text analysis of unstructured content and organizational data
- Optimization and simulation for identifying potential scenarios and outcomes
DataCrunch Lab applies machine learning solutions to a variety of tasks:
- Fraud detection
- Marketing campaign optimization
- Operational risk reduction
- Data visualization
Data visualization can be utilized in the identification of areas that require attention or improvement, factors influencing customer behavior, optimal placement of products, emerging trends, sales volume prediction, and patterns.
The company’s IoT solutions comprise data security and management and access controls for integration with existing workflows, analytics, and business intelligence tools, as well as remote monitoring and control of systems.
DataCrunch Lab’s quality assurance systems use Radio Frequency Identification (RFID), sensors, video monitoring, remote information distribution, and cloud solutions on the production line to simplify inspection procedures and reduce costs caused by quality defects. The company's machine vision methods aim to optimize logistics and transfer processes, in particular concerning the communication between products and manufacturing equipment. The company's quality assurance solutions include those below:
- Digital assistants
- Text analytics
- Computer vision systems
DataCrunch Lab conducts a strategy workshop involving collaboration on the development of a custom solution based on the client's needs. The focus of the workshop is on converting the strategic objectives of the client organization into a roadmap to address the business challenges.
DataCrunch Lab offers training for employees focused on the application of various tools in their workflow. The following are some of the topics discussed:
- Basic and advanced techniques
- Methodology
- Use of software
- Hands-on workshops
DataCrunch's advisory services help clients address various issues related to strategic objectives:
- Technology limitations
- AI suitability within the company's system
- Potential applications of machine learning within the client's company
- How data can be utilized
- The necessary characteristics of the platform
DataCrunch Lab offers an integration platform that consolidates structured and unstructured data and delivers it to any system on a scalable data platform for Big Data, AI, and IoT projects. The platform offers the following services:
- Design of the migration, integration plan, and data synchronization with modeling tools
- Development of the integration process
- Data integration services, such as data import, data export, data replication, and data synchronization
- Cloud computing
- On-premise solution deployment and configuration
- Design and deployment of edge computing solutions
DataCrunch Lab serves clients in multiple industries, including IT, legal, finance, government, manufacturing, and retail sectors.
Some of the common use cases in the financial sector include the following:
- Customer analytics
- Customer lifetime value
- Fraud detection
The company's solutions utilize financial, operational, social, and other government-related data for a variety of applications, which in turn helps federal agencies and other public and private sector organizations. Benefits include faster processes enabled by intelligent automation, fraud detection, waste, and abuse using machine learning. Some of the use cases in government include the following:
- Improvements in systems related to citizen services
- Guidance in financial decisions
- Optimization of operational processes
Some of the use cases of the company's solutions in the manufacturing sector include those below:
- Small and large-scale data analytics
- Quality control
- Cost reduction through predictive maintenance
- Defect tracking in manufacturing processes
Some of the use cases of DataCrunch Lab’s solutions in the retail industry include the following:
- Recommendation engines
- Market basket analysis
- Personalized marketing
- Inventory management
- Finding locations for new stores
- Customer sentiment analysis
- Lifetime value prediction
