AI-Powered Financial Research Platform

An investment research operation needed to aggregate and analyze data from multiple fragmented financial data sources. Analysts were spending hours manually pulling data from different platforms, cross-referencing information, and generating reports. They needed an AI-powered solution that could query multiple sources intelligently, respect API rate limits, and maintain conversation context across research sessions.

We developed a multi-model AI research agent with tool and function calling capabilities across 5 financial data sources. The system uses intelligent rate-limit-aware query routing to maximize data retrieval without hitting API throttles. It orchestrates between Claude, Gemini, and OpenRouter models, routing each research task to the best-fit model. Persistent conversation history allows analysts to build on previous research sessions. The platform includes brokerage CSV ETL pipelines and MCP server integration for extensibility.

Research time per analysis was cut significantly. The multi-model approach provides more comprehensive insights by leveraging different AI strengths. Rate-limit-aware routing eliminated failed queries and maximized data source utilization. Analysts can now conduct deep research across all data sources from a single conversational interface.