The Challenge
The organization (which supports investors by analyzing financial reports and historical data) was facing delays and missed opportunities due to outdated research workflows. Analysts had to juggle multiple tools, structure their own SQL queries, and then interpret results buried in raw tables. As query volumes grew, reliance on data specialists increased, which led to longer response times.
The organization needed a solution that could:
- Allow any analyst to ask a question in plain English
- Retrieve results in real-time from a secure database
- Provide explainable answers and visualizations
- Minimize hallucination and improve historical context retention
- Auto-analyze the financial documents to provide future outlooks on investments focusing on Country, Region and Macro-Economic factors to feed their Dashboards and other tools.
A Day in the Life: Before Our Solution
A simple question from a portfolio manager like, “How did green energy stocks perform compared to oil last quarter?”, would kick off a long, manual slog for the analyst:
- Dig into SQL documentation or reuse an old query
- Pull price data from scattered sources
- Clean and chart it in Excel or a BI tool
- Package the takeaway into a slide deck
This process could take hours. With multiple such requests daily — and often under time pressure — the research team was constantly playing catch-up. Even small variations in phrasing, like "last quarter" versus "last 90 days," meant rebuilding the entire workflow.
If a portfolio manager asked for a forecast on China’s GDP, the analyst had to manually comb through IMF and World Bank reports, extract relevant data, interpret economic signals, and translate them into a usable forecast.
By the time the insight reached decision-makers, it was already outdated. Speed and precision were both compromised, and valuable opportunities were often missed.
Pain Points:
- Analysts spent up to 40% of their time crafting database queries, not analyzing opportunities
- Recurrent effort up to 50% of human effort was spent in analyzing financial documents every quarter/year
- Non-technical teammates had limited access to insights
- Research teams faced delayed market responses
- Inconsistent query results created friction and uncertainty