Maya

Maya.ai's modular AI platform accelerates business transformation by providing flexible deployment options, powerful model libraries, and robust data capabilities. This comprehensive architecture enables rapid AI implementation across various business contexts, from cold start with anonymized data to warm start with transaction data. The platform supports multiple deployment routes, including enterprise data mart creation and custom data mart development, while offering secure data sharing and diverse integration approaches. Underpinning this technical foundation are 10 core models and 10 data capabilities that deliver high performance and rapid deployment. Through low-code tools and comprehensive genome building capabilities, businesses can create thousands of custom data structures while leveraging pre-built modules covering every industry and use case. This architecture has driven measurable business impact, including $5 billion in incremental spend across a portfolio managing 3 billion transactions while serving 53 million customers and 7 million merchants.

Maya.ai Platform Architecture

The Maya.ai platform architecture centers on flexible app deployment, powerful model libraries, and robust data capabilities, designed for rapid AI implementation across various business contexts. The platform supports multiple deployment routes, from cold start with anonymized data to warm start with transaction data, while offering both enterprise data mart creation options and custom data mart development.

For data management, the platform enables secure data sharing via anonymized connectors and supports diverse deployment approaches including white-label solutions and headless APIs for system integration. Post-deployment, businesses can leverage modular App Store features and expert support for accelerated growth.

The platform's technical foundation consists of 10 core models designed for high performance and rapid deployment, including engines for data cleansing, mapping, enrichment, and exploration. Powered by proprietary algorithms and patented technologies, these models enable rapid development through low-code tools and comprehensive genome building capabilities.

Data management stands on 10 core capabilities that simplify integrations through pre-built connectors, accelerate data migration, and provide robust governance features including automated monitoring and fraud detection tools. These technical components support the platform's broader mission of delivering personalized customer experiences while driving business innovation across consumer banking, digital payments, and other key sectors.

Deployment Options and Integration

Deployment Strategies

The platform's deployment strategy offers businesses multiple entry points, from initial setup with anonymized data to scaling with custom data mart development. The foundation of the deployment process includes two primary approaches: enterprise data mart creation and custom data mart development.

App Deployment

App deployment follows several structured steps, beginning with the creation of an enterprise data mart. This initial setup provides a scalable foundation for AI implementation, allowing businesses to transition between deployment stages as needed.

For customers seeking a faster implementation path, the platform supports warm start deployment using existing enterprise data. This method enables businesses to leverage their existing infrastructure while speeding up the deployment process.

The deployment journey continues with the option to run out-of-box models, configure existing models, or create custom models based on specific business requirements. Each step builds upon the previous one, allowing businesses to scale their AI initiatives incrementally.

Data Sharing

Data sharing between the platform and external systems occurs through secure, anonymized connectors that enable seamless integration. These connectors support multiple deployment paths, including both cold start and warm start options, depending on the initial data availability.

Solution Customization

The platform offers businesses the flexibility to customize their deployment solutions according to specific needs. This customization extends to both delivery methods and integration approaches, allowing businesses to choose between white-label solutions, progressive web apps/widgets, or fully integrated APIs with customizable front ends.

The integration capabilities include headless APIs designed to enhance performance, agility, and scalability within existing systems. Businesses can leverage these capabilities by working with existing merchant partners, sourcing local integration partners, or connecting with global aggregators and affiliates.

Post-Go-Live Features

After successful deployment, businesses have access to additional resources designed to drive continued growth and innovation. The platform's modular App Store features enable businesses to accelerate their transformation by embedding expert support for adoption, innovation, and opportunity discovery.

Through these comprehensive deployment options and integration capabilities, Maya.ai delivers rapid AI implementation while providing the flexibility required for businesses of all sizes and maturity levels.

AI Model Development and Deployment

Model Development Workflow

The AI model development process on Maya.ai revolves around pre-built libraries and low-code tools that significantly reduce development time and complexity. The platform houses hundreds of pre-built insight modules covering every industry and use case, allowing businesses to deploy models quickly and efficiently.

Developers begin by selecting from the platform's extensive library of models, which includes capabilities for data cleansing, mapping, enrichment, and exploration. These pre-built models leverage proprietary algorithms and patented technologies to standardize and enhance data quality while requiring minimal configuration.

For businesses needing customized solutions, the platform offers a low-code environment that enables rapid model development without extensive coding expertise. Through the Genome Builder tool, users can create thousands of custom data structures and models specific to their business needs.

High-Performance Workflows

Maya.ai's deployment process focuses on optimizing model performance while minimizing development time. The platform's workflow design enables users to run out-of-box models directly after initial setup, while those requiring customization can configure existing models or create entirely new ones through the provided tools.

The process begins with creating an enterprise data mart, which serves as a scalable foundation for AI implementation. For businesses already using enterprise data, the warm start option allows for faster deployment by leveraging existing infrastructure. This modular approach enables businesses to scale their AI initiatives incrementally while maintaining high performance standards.

Data Management and Collaboration

The platform's data management capabilities revolve around ten core functionalities that simplify integration through pre-built connectors, accelerate data migration, and provide robust governance features including automated monitoring and fraud detection tools.

Pre-built connectors enable seamless data sharing between the platform and external systems, supporting diverse deployment approaches including both cold start and warm start options. Data migration processes are designed to accelerate the implementation journey, while data ingestion and integration capabilities onboard and unify data from disparate sources and ecosystem partners.

Data governance focuses on tracking data lineage, managing metadata, and mitigating data security risks. Automated data monitoring tools optimize governance and quality control, while fraud and risk monitoring workflows are automated, trained, and scalable using artificial intelligence and machine learning technologies.

Additional capabilities include automated data quality auditing to unlock cost savings and mitigate risks by improving data access, performance, and security. The platform also supports next-generation capabilities like generative artificial intelligence (GenAI) and natural language processing (NLP), which facilitate business innovation at all levels of operation.

Business Applications and Industry Impact

Consumer banking benefits from the platform's AI-led cross/up-sell models and personalized marketplaces, while digital payments gain from enhanced AI insights and upsell capabilities. The platform's technology foundation includes patented recommendation systems, entity relationship mapping, and data analytics, supported by a comprehensive suite of modular applications.

Crayon Data serves visionary enterprises, dynamic startups, and data enthusiasts, providing AI-driven solutions tailored to their specific needs. The company has demonstrated measurable business impact through a $5 billion incremental spend across their portfolio, serving 53 million customers, 7 million merchants, and managing a $80 billion portfolio size with 3 billion total transactions.

The platform's deployment process supports three distinct business maturity levels, enabling rapid adoption regardless of existing AI infrastructure. From cold start with anonymized data to warm start with transactional data, businesses can choose between creating enterprise data marts, leveraging existing marts, or building custom data solutions. These flexible deployment options support diverse integration needs through headless APIs, white-label solutions, and fully integrated progressive web apps/widgets.

Post-deployment, the platform accelerates business growth through its modular App Store capabilities, offering expert support for adoption, innovation, and opportunity discovery. The comprehensive suite of applications supports diverse digital experiences, from customer-facing widgets to enterprise-level data services, while the underlying model library enables rapid deployment of pre-built and custom models through low-code development tools.