Normalized claim
Quantified impact: 20% increase
Increased assets under management (AUM) by over 20% without increasing workforce size.
Clearwater Analytics revolutionizes investment management using generative AI and Amazon SageMaker JumpStart
Use case typeRisk assessmentUpdated Jun 13, 2026
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.
- Organization
- Clearwater Analytics
- Industry
- Finance
- Location
- United States
- Published
- December 2024
Reported outcomes
Impact: More than 20% higher
Other quantified impact
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Productivity: 25-43%
Achieved 25% to 43% efficiency gains in internal operations.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Clearwater Analytics
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Machine Learning Blog
Accelerate revenue generation and operational efficiency with AI-driven tools in capital markets
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Generative AI for Investment Management
- 2AI-Driven Workflow Automation
- 3Investment Risk Management
- Built generative AI chat assistants for customers and employees using fine-tuned LLMs hosted on Amazon SageMaker JumpStart.
- Used domain adaptation to fine-tune models on investment management data, reducing need for manual data labeling.
- Employed RAG to fuse domain documentation with LLM responses, enhancing knowledge accuracy and model relevance.
- Implemented an internal evaluation framework and automation pipeline (LLMOps) for model performance and deployment optimization.
Architecture
Architecture integrates Amazon SageMaker JumpStart for hosting foundation and fine-tuned LLMs, RAG for domain knowledge augmentation, and an internal LLMOps pipeline for evaluation and deployment automation.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Quantified outcome available
- Technical implementation details available
ExpandedExpanded
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2026.
Measures whether this deployment's public evidence persists — not whether the system is still in production.
Type: Blog PostPublished: Dec 13, 2024Publisher: AWS Machine Learning BlogEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
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