Case Study: Investment Research CoPilot

Capital Markets

Investment Research CoPilot

The Challenge: Information Overload and Analysis Bottlenecks

A global investment bank was struggling with the exponential growth of financial information and market data. Their research analysts were drowning in a deluge of data, processing over 1,500 quarterly earnings calls weekly during peak seasons and analyzing more than 1,000 research documents daily while cross-referencing market data across 25+ providers. The overwhelming volume meant that senior analysts were spending 70% of their time on information gathering rather than high-value analysis. This inefficiency led to missed investment opportunities, estimated at $150M annually in lost potential revenue. Additionally, the bank faced growing competitive pressure from boutique firms that were already leveraging advanced analytics, threatening their market position and ability to attract top talent.

Our Solution: AI-Powered Research Assistant

Lydatum designed and implemented a comprehensive AI-powered research assistant platform that revolutionized how analysts interact with financial information. The solution included:

• Intelligent Document Processing: We developed an advanced natural language processing pipeline using Azure OpenAI Service with GPT-4 that could automatically analyze earnings calls transcripts, research reports, and financial news. The system extracted key insights, identified market sentiment, and flagged critical information for analyst review.
• Unified Knowledge Platform: Using Pinecone's vector database for semantic search capabilities, we created a centralized knowledge repository that could instantly surface relevant historical research, market precedents, and cross-asset correlations. Azure Synapse Analytics provided the data warehouse backbone, enabling real-time analytics across vast datasets.
• Research Workflow Automation: Through Apache Airflow and Azure Data Factory, we orchestrated end-to-end research workflows that automated routine tasks like data gathering, preliminary analysis, and report generation. Redis-powered caching ensured lightning-fast access to frequently needed data.

The implementation process was carefully managed through extensive collaboration with stakeholders, including 30+ workshops with research teams and compliance officers. A hybrid team of 15 technical and domain experts developed the initial MVP focused on US equities, followed by a comprehensive training program for 200+ analysts. Built-in compliance guardrails and audit trails ensured regulatory requirements were met throughout.

The Impact: Transformative Efficiency Gains and Revenue Growth

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

The solution's impact extended beyond pure efficiency gains. The platform saved over 120,000 analyst hours annually, allowing senior researchers to focus on complex analysis and client engagement. The high adoption rate of 95% among analysts demonstrated the platform's value, while the 200% ROI in the first year validated the investment decision. Perhaps most significantly, the project catalyzed the creation of a new AI-augmented analyst career path, positioning the bank as an innovative leader in the industry and helping attract top talent.

Technologies Used: Azure OpenAI Service (GPT-4), Pinecone, Apache Airflow, Azure Data Factory, Azure Synapse Analytics, Redis