A top AI lab needed to strengthen their model’s financial reasoning—specifically its ability to handle multi-step analyses and complex, hypothetical finance scenarios. The key challenge was sourcing qualified finance professionals who could generate and evaluate detailed domain-specific datasets under tight deadlines.
Databrewery activated its Brewforce network to quickly onboard a team of financial experts with CFA, MBA, Master’s, and PhD-level qualifications. Using Databrewery’s platform, the project was set up with precise instructions, real-time quality checks, and custom workflows for evaluating nuanced financial logic.
The team delivered high-quality, differentiated data through structured evaluation and ranking. With consistent access to vetted finance experts, the AI lab now has the foundation to improve its model’s performance on specialized financial tasks and scenarios.
A leading AI lab building advanced LLMs set out to improve its model’s performance on complex finance-related questions aiming for more accurate, insightful responses tied to real-world financial data like ticker symbols and company reports. They also wanted the model to handle the kind of questions a financial analyst would ask.
But the task demanded deep domain expertise and fast execution. With limited internal bandwidth and a high bar for quality, the lab partnered with Databrewery to quickly source and manage a team of qualified financial professionals each with credentials like CFA, MBA, or advanced degrees—to evaluate and rank model responses.
Databrewery quickly assembled a vetted team of finance professionals to take on the task sourcing experts with CFA certifications, MBAs, and PhDs through its Brewforce network. With a 24-hour calibration period and full project oversight, the lab relied on Databrewery to deliver accurate, high-quality data grounded in real financial logic.
This project also gave domain experts a rare chance to apply their industry skills to cutting-edge AI development.
“Contributing to this initiative allowed me to bridge real-world finance with AI. It pushed me to think critically about how models interpret nuanced financial data and scenarios.” – Neeraj M., MBA, equity research specialist
The AI lab had a clear vision, they needed a detailed, domain-specific financial dataset built from complex documents and tightly defined instructions. Working closely with Databrewery, they mapped out the approach inside the Databrewery platform. A custom ontology was created using the text editor, complete with granular classifications, sub-classifications, and structured free-text inputs to guide the expert labelers.
Building the right team was critical. Databrewery reviewed over 50 financial professionals through its Brewforce network, handpicking those most qualified to handle the nuanced task. These experts were assigned to rank outputs from the model on a 1–5 scale, evaluating various components of hypothetical financial scenarios, probability, feasibility, argument strength, and causal reasoning.
With real-time visibility into performance metrics and feedback loops in place, the AI lab had full transparency throughout. Project workflows were refined as needed to ensure output met expectations.
By the end, the AI lab had what it needed: accurate, expert-verified datasets delivered on a tight deadline. The project significantly boosted the model’s reliability in financial reasoning and set a strong foundation for future work in the domain.