Investment Management Firm
Explore Project
Data Infrastructure + AI Modeling
Core Capacities
Institutional Finance
Project Focus
50K+ data points structured
Scale of Impact
2025
Year
Overview
1
The firm’s analysts worked with large datasets and complex financial instruments, requiring hours of manual review and synthesis. Producing research reports was time-intensive and left little capacity to explore new opportunities. Leadership wanted to explore AI solutions but needed assurance that outputs would be dependable, explainable, and compliant with internal and regulatory standards.
2
Sierra partnered directly with the research division to scope and implement targeted AI experiments. By embedding engineers alongside analysts, the project focused on retrieval-augmented generation (RAG), fine-tuned prompting, and evaluation frameworks to measure output accuracy. The goal was to prototype workflows where AI augmented analyst expertise rather than replaced it.
3
• Shadowed analysts to understand the full research lifecycle from data gathering to reporting. • Built RAG pipelines that allowed AI systems to ground outputs in verified financial documents. • Designed prompting strategies tailored to financial modeling and commentary. • Developed internal benchmarks to measure reliability and surface areas for refinement.
4
The collaboration demonstrated that AI could accelerate research and improve consistency in client reporting. Analysts gained access to tools that reduced repetitive work while maintaining control over final insights. By combining financial subject matter expertise with forward-deployed engineering, Sierra helped the firm chart a practical path for integrating AI into high-stakes research environments.
