Valuefy Data Copilot
Built a multi-tenant AI Data Copilot using LangGraph, LangChain and RAG, enabling users to query live portfolio, transaction, holdings and client data through natural language.
Business Challenge
Relationship managers, operations teams and users depended heavily on data teams to retrieve and interpret portfolio, transaction, holdings and client information. Existing workflows required navigating multiple data sources and manual requests, creating delays and limiting self-service access to live wealth-management data.
Solution Delivered
Architected and developed a multi-tenant conversational AI Data Copilot using LangGraph and LangChain with RAG and LLM-based tool orchestration. The system translates natural-language questions into structured data retrieval workflows and provides contextual responses over live portfolio, transaction, holdings and client data. Implemented tenant-aware access controls, data-source integrations, conversational workflows and production deployment patterns for enterprise wealth-management use cases.
Outcomes Achieved
The Data Copilot enabled users and relationship managers to access supported wealth-management information through natural language instead of relying on repeated manual data-team requests. It reduced dependency on the data team by approximately 90% for supported workflows and was deployed across 100+ live tenants.
Measurable Business Outcome
Reduced dependency on the data team by approximately 90% for supported data-access workflows. The solution was deployed across 100+ live tenants, enabling users to interact with supported portfolio, transaction, holdings and client data through natural-language queries.