Research organizations serve as the backbone of policy development, analyzing critical data to suggest systemic improvements. TrustBridge, one such leading research organization, actively engages in studying regulatory enforcement actions and recommending reforms. In the course of its mission, TrustBridge is often faced with the task of extracting meaningful insights from vast and disorganized regulatory order datasets. It found itself at a crossroads — one where manual processes struggled to keep up with their ambitions.
TrustBridge is often tasked with extracting patterns and insights from orders issued by various regulators and regulatory tribunals. These documents are key to understanding regulatory trends and proposing reforms. Each project requires identification of specific data points, referred to as “indicators,” that help analyze the orders in a meaningful way.
These indicators vary across projects. For instance, one project - on orders of the Securities Appellate Tribunal - required TrustBridge to extract 80 unique indicators from a dataset of over 500 tribunal orders. The extracted information was then collated into detailed reports, forming the basis for implementable recommendations.
Despite the clear objectives, the process is far from straightforward. Relying heavily on manual data extraction, TrustBridge researchers meticulously combed through hundreds of orders, identifying and recording data points in line with the indicators. While this method ensured that the insights were grounded in a thorough understanding of each document, it also introduced several challenges.
The purely manual approach demands a significant investment of time and personnel. Large teams are needed to process hundreds of documents on a specific set of indicators. Scaling these efforts for larger projects can considerably strain an organization’s resources.
Despite rigorous training and standard operating procedures, the process of interpreting complex indicators is prone to subjectivity. Researchers often encounter nuances in the text that could lead to slight variations in data interpretation, diluting the uniformity of results. The sheer volume of data necessitates a likelihood of human error. Further, repetitive data extraction leaves little time for researchers to focus on more analytical work.
The Lucio Solution
TrustBridge partnered with Lucio to reimagine their approach to studying regulatory enforcement action. Lucio’s AI-driven tools allowed TrustBridge to automate their data extraction processes, ensuring accuracy, consistency, and scalability.
Lucio worked closely with TrustBridge to understand the unique requirements of their projects. TrustBridge is now able to define indicators for each of their projects. Lucio instantly reviews the orders, and extracts all details required to complete indicators. Lucio’s AI review accounts for a substantive and contextual analysis of each document. Moreover, TrustBridge researchers can easily audit Lucio’s output through clickable hyperlinks to source pages.
Finally, Lucio converts the responses into strict output types desired by TrustBridge researchers. Thus, Lucio has helped reimagine the data extraction workflow at TrustBridge. Earlier an entirely manual effort, bulk data extraction is now largely automated in a manner that ensures accuracy, time-savings and cost efficiency. This frees up crucial hours for TrustBridge researchers, allowing them to spend more time on analyzing extracted data.
A New Chapter in Research
The partnership between TrustBridge and Lucio represents a significant step forward in the field of regulatory research. By embracing AI-driven solutions, TrustBridge has not only improved the efficiency of their operations but also set a precedent for how technology can be leveraged to drive systemic change.
Research organizations serve as the backbone of policy development, analyzing critical data to suggest systemic improvements. TrustBridge, one such leading research organization, actively engages in studying regulatory enforcement actions and recommending reforms. In the course of its mission, TrustBridge is often faced with the task of extracting meaningful insights from vast and disorganized regulatory order datasets. It found itself at a crossroads — one where manual processes struggled to keep up with their ambitions.
TrustBridge is often tasked with extracting patterns and insights from orders issued by various regulators and regulatory tribunals. These documents are key to understanding regulatory trends and proposing reforms. Each project requires identification of specific data points, referred to as “indicators,” that help analyze the orders in a meaningful way.
These indicators vary across projects. For instance, one project - on orders of the Securities Appellate Tribunal - required TrustBridge to extract 80 unique indicators from a dataset of over 500 tribunal orders. The extracted information was then collated into detailed reports, forming the basis for implementable recommendations.
Despite the clear objectives, the process is far from straightforward. Relying heavily on manual data extraction, TrustBridge researchers meticulously combed through hundreds of orders, identifying and recording data points in line with the indicators. While this method ensured that the insights were grounded in a thorough understanding of each document, it also introduced several challenges.
The purely manual approach demands a significant investment of time and personnel. Large teams are needed to process hundreds of documents on a specific set of indicators. Scaling these efforts for larger projects can considerably strain an organization’s resources.
Despite rigorous training and standard operating procedures, the process of interpreting complex indicators is prone to subjectivity. Researchers often encounter nuances in the text that could lead to slight variations in data interpretation, diluting the uniformity of results. The sheer volume of data necessitates a likelihood of human error. Further, repetitive data extraction leaves little time for researchers to focus on more analytical work.
The Lucio Solution
TrustBridge partnered with Lucio to reimagine their approach to studying regulatory enforcement action. Lucio’s AI-driven tools allowed TrustBridge to automate their data extraction processes, ensuring accuracy, consistency, and scalability.
Lucio worked closely with TrustBridge to understand the unique requirements of their projects. TrustBridge is now able to define indicators for each of their projects. Lucio instantly reviews the orders, and extracts all details required to complete indicators. Lucio’s AI review accounts for a substantive and contextual analysis of each document. Moreover, TrustBridge researchers can easily audit Lucio’s output through clickable hyperlinks to source pages.
Finally, Lucio converts the responses into strict output types desired by TrustBridge researchers. Thus, Lucio has helped reimagine the data extraction workflow at TrustBridge. Earlier an entirely manual effort, bulk data extraction is now largely automated in a manner that ensures accuracy, time-savings and cost efficiency. This frees up crucial hours for TrustBridge researchers, allowing them to spend more time on analyzing extracted data.
A New Chapter in Research
The partnership between TrustBridge and Lucio represents a significant step forward in the field of regulatory research. By embracing AI-driven solutions, TrustBridge has not only improved the efficiency of their operations but also set a precedent for how technology can be leveraged to drive systemic change.
