Case Study: Developing Industry-Specific AI Solutions

Cloud Providers

Developing Industry-Specific AI Solutions

The Challenge: Bridging the Gap Between Platform Power and Vertical Needs

A major global cloud provider (in this case, AWS, based on the technologies) offered a powerful suite of AI/ML and data services, including Amazon SageMaker and robust data warehousing capabilities often paired with partners like Snowflake. However, they recognized that simply providing the tools wasn't enough to capture high-value enterprise customers in specialized industries like financial services, healthcare, or manufacturing. These industries required solutions tailored to their specific workflows, regulatory requirements, and data types. The cloud provider lacked the deep, in-house domain expertise required to build these sophisticated, industry-specific AI solution templates and accelerators, creating a gap in their go-to-market strategy for certain key verticals and potentially ceding ground to competitors with more targeted offerings.

Our Solution: Partnership for Bespoke Marketplace Accelerators

Lydatum entered into a strategic partnership with the cloud provider (AWS) to co-develop industry-specific AI solutions, leveraging Lydatum's deep domain expertise and AWS's powerful cloud platform. The initial focus was on creating risk modeling templates for the financial services industry, designed to be offered through the AWS Marketplace:

• Domain Expertise Integration: Lydatum provided expert knowledge in financial risk modeling (e.g., credit risk, market risk, fraud risk), regulatory compliance (e.g., Basel III, CCAR), and common financial data structures.
• Solution Architecture on AWS: We designed reusable solution architectures utilizing AWS native services. Amazon SageMaker was used for developing, training, and deploying the core machine learning models for risk assessment. Snowflake (often used alongside AWS services) served as the high-performance data platform for handling large volumes of financial data, feature engineering, and storing model results, showcasing seamless integration within the AWS ecosystem. Services like AWS Glue for data cataloging and AWS Step Functions for workflow orchestration were also incorporated.
• Development of Reusable Templates & Accelerators: The core deliverable was a set of well-documented, configurable solution templates. These included pre-built SageMaker notebooks, CloudFormation or CDK scripts for infrastructure deployment, sample data schemas for Snowflake, and example dashboards. These templates acted as accelerators, allowing financial institutions using AWS to rapidly deploy and customize sophisticated risk modeling capabilities on their own cloud environment.
• Marketplace Packaging: Lydatum assisted AWS in packaging these solutions for deployment via the AWS Marketplace, including documentation, deployment guides, and best practice recommendations.

The Impact: Expanded Portfolio, New Customers, Platform Showcase

The partnership yielded significant benefits for the cloud provider and its customers:

The new risk modeling solutions significantly expanded AWS's portfolio of industry-specific offerings on their marketplace, making their platform more attractive to financial institutions. These ready-to-deploy templates lowered the barrier to entry for banks and investment firms looking to leverage advanced AI for risk management, directly contributing to the acquisition of new enterprise customers in this key vertical. Furthermore, the solutions served as powerful showcases for the capabilities of Amazon SageMaker and its seamless integration with leading data platforms like Snowflake, reinforcing AWS's value proposition as a comprehensive platform for enterprise AI and analytics. This successful collaboration provided a model for developing further industry-specific solutions.

Technologies Used: Amazon SageMaker, Snowflake, AWS CloudFormation/CDK, Amazon S3, AWS Glue, AWS Step Functions, Python