Clearwater Analytics
Clearwater Analytics has 2 source-linked AI deployments documented in AIUseCaseHub, across 1 industry and 1 country.
Hyperscaler mix
See whether Clearwater Analytics's cases are powered by Microsoft, AWS, GCP, or multiple providers.
How Clearwater Analytics builds AI
Build / Buy / Compose across this company's documented cases
2 of 2 cases classified (100%) · Compare all use-case types
Reported outcomes
2 cases report measurable results
+34%
Productivity & throughput
median · 2 metrics
+20%
Revenue & growth
median · 1 metric
Medians of results published in Clearwater Analytics cases, normalized for comparability. See all benchmarks →
Evidence persistence
1 of 1 judgeable case is still publicly referenced · 1 show the organization expanding AI use.
Durability of public evidence, not whether systems remain in production. How this is measured →
Technology snapshot
What Clearwater Analytics uses across visible cases
4 named technologies are mentioned across 2 cases, led by Amazon Bedrock.
Capability mix
No capability flags are attached to these cases yet.
All Use Cases (2)
Clearwater Analytics: Generative AI Transformation in Financial Services with Amazon Bedrock
Clearwater Analytics transformed investment management workflows by developing the Clearwater Intelligent Console (CWIC) suite, a generative AI-powered platform built using AWS Amazon Bedrock.The platform evolved from a simple chat interface into a sophisticated AI system that enables faster responses and reduces service tickets, contributing to a 20% business growth without increasing headcount.AWS provided the infrastructure, data foundations, and security to enable this AI transformation in financial services.
Clearwater Analytics revolutionizes investment management using generative AI and Amazon SageMaker JumpStart
Clearwater Analytics is a global SaaS provider for investment management and reporting with over $7.3 trillion in assets managed.The company developed generative AI applications using Amazon SageMaker JumpStart with large language models (LLMs) to enhance internal workflow and customer solutions.Implemented Retrieval Augmented Generation (RAG) and fine-tuned domain-adapted models to deliver specialized knowledge and improved response times.Developed AI assistants for customer-facing, internal, and domain-specific investment management tasks, achieving substantial workflow automation and knowledge management.The approach includes sophisticated model evaluation pipelines and domain adaptation techniques for continuous improvement and deployment of AI models.