Case Study: Investment Research CoPilot

Capital Markets

Investment Research CoPilot

The Challenge: Information Overload and Analysis Bottlenecks

A global investment bank's research analysts were spending 70% of their time on information gathering instead of high-value analysis. Analysts were processing 1,500+ earnings calls weekly during peak seasons and 1,000+ research documents daily across 25+ data providers. The overwhelming volume led to missed investment opportunities and growing competitive pressure from firms already leveraging advanced analytics.

Our Solution: AI-Powered Research CoPilot

Lydatum designed and built an AI-powered research CoPilot using Azure AI Foundry with a multi-agent architecture where specialized agents each handled distinct tasks:

• Multi-Agent Research Pipeline: One agent parsed and analyzed earnings transcripts, another cross-referenced findings against historical research and market precedents, a third analyzed public sentiment and press mentions, a fourth calculated fundamentals, and a fifth synthesized outputs into structured insights for analyst review. Agents were orchestrated to work in parallel, with a final synthesis agent aggregating and validating all upstream outputs and producing the final results based on each agent's signals and analysis.
• Unified Knowledge Platform: A vector database platform with semantic search enabled retrieval of historical research, market precedents, and cross-asset correlations, serving as the shared memory layer that agents queried and contributed to throughout the research workflow.
• Agentic Workflow Orchestration: Research workflows were automated through agent orchestration, with each agent step producing traceable inputs and outputs, full audit trails of every action taken, and built-in quality checks before finally presenting results to analysts, ensuring it accelerated human decision-making without replacing it.

The platform was initially deployed against US equities to validate agent behavior in a well-understood domain before expanding to broader asset classes and geographies. Compliance guardrails were embedded from day one, with built-in audit trails tracking every AI-generated recommendation back to its source data, capturing all input and output paths, and ensuring full traceability and replayability of agents' actions. Compliance officers were included in requirements-defining workshops alongside research teams, so the system was shaped by regulatory reality from the start.

The Impact: Transformative Efficiency Gains and Revenue Growth

The implementation of Lydatum's AI-powered research CoPilot delivered remarkable results across multiple dimensions:

Research preparation time was reduced by 65%, freeing analysts to focus on complex analysis and client engagement. Analyst coverage capacity increased 4x, meaning the same team could now cover four times the number of securities and sectors. The platform saved over 120,000 analyst hours annually, with a 95% adoption rate among analysts and 200% ROI in the first year. The AI was intentionally positioned as a CoPilot, explicitly designed to not operate as an autonomous decision-maker — senior analysts remained at the core of decision-making, with the system surfacing and synthesizing relevant information to inform their judgment.

Technologies Used: Azure AI Foundry, Vector Database, Semantic Search, Agent Orchestration Framework