AI Risk Analytics Platform
Morgan Stanley
15+
Engineering teams supported by SDKs and integration tools
40%
Internal onboarding-time reduction helped achieve
Overview
At Morgan Stanley, I worked across product and technical delivery for AI-driven risk analytics, including enterprise retrieval and developer integration tools.
The work connected approved financial data, retrieval, structured model outputs, and operational dashboards. Access controls, traceability, and model-governance requirements shaped the product.
I delivered Python SDKs, reference notebooks, API playgrounds, and self-service sandboxes for more than 15 engineering teams, helping reduce internal onboarding time by 40%.
The Problem
Teams needed a clearer path to integrate risk-analytics capabilities while preserving access controls and the evidence behind model outputs. The product work had to address both the model workflow and the developer experience around it.
My Role
Product Team Lead
As Product Team Lead, I translated stakeholder needs into requirements, acceptance criteria, API contracts, and delivery priorities. I worked with engineering, research, risk, and security colleagues on the path from prototype to production and contributed developer-facing integration tools.
The Approach
Connected enterprise retrieval over approved sources with structured outputs and evaluation loops for risk-reporting workflows.
Defined latency, explainability, observability, and access-control requirements with the teams responsible for delivering and operating the services.
Built developer enablement around Python SDKs, notebooks, API playgrounds, and integration guidance so teams could understand and use the capabilities.
Key Features
What we built
Grounded Risk Workflows
Enterprise RAG over approved data sources, with structured outputs and evaluation loops.
Developer Integration Tools
Python SDKs, reference notebooks, API playgrounds, and self-service sandboxes for engineering teams.
Operational Visibility
Monitoring, tracing, and feedback workflows to support investigation and product iteration.
Access and Governance
Product requirements for authorized retrieval, explainability, data lineage, and auditability.
Tech Stack
Key Lessons
What I took away from this project
Developer integration is part of the product, not a separate handoff.
Financial AI needs traceable evidence and clearly defined access boundaries.
More Projects
Explore other work
Want to build something similar?
I help companies scale their products and build high-performing teams. Let's discuss how I can help with your next project.
Get in Touch