Evaluating poverty interventions traditionally involved time-consuming and labor-intensive processes, with researchers manually sifting through extensive literature, extracting relevant data, and constructing impact pathways. This method was not only inefficient but also susceptible to errors and biases. FINCA required a customized solution to streamline this process, offering real-time insights to enhance decision-making and expedite the journey to knowledge.
Calance designed a Customized Automated Research Assistant (AuRA) for FINCA. It was specifically designed to meet FINCA’s unique needs, utilizing generative AI technologies to provide a comprehensive, efficient, and user-friendly platform for assessing poverty solutions.
Scalable Computing Infrastructure: Built on Amazon Elastic Kubernetes Service (EKS) for optimal efficiency and scalability, AuRA utilizes Apache Airflow to automate processes, ensuring a seamless user experience.
Dynamic Data Library: Upon query submission, AuRA identifies, retrieves, and stores relevant documents from a wide range of sources, including Google Scholar, the World Bank, and user-specified inputs. This data library features key document attributes and allows for easy editing, merging, and sharing among researchers.
Advanced Topic Modeling: Researchers can select their preferred large language model (LLM) such as Anthropic, Gemini, or ChatGPT. AuRA leverages the LLM to generate an initial impact pathway map, outlining topics and subtopics relevant to the research query. Retrieved documents are tagged to pertinent subtopics, providing a structured overview of the data.
Interactive Curation: AuRA empowers researchers to actively curate their data libraries and impact pathways. They can add or remove documents, trigger additional waves of document retrieval, and modify the topic model to better represent their research focus.
Since its deployment, AuRA has significantly enhanced impact measurement in philanthropy and social investing, offering the following benefits:
Enhanced Efficiency: Researchers can rapidly access and analyze relevant literature, reducing the time and effort required for impact assessment.
Improved Decision-Making: AuRA’s structured data and interactive features provide clear insights, facilitating better-informed decisions on poverty interventions.
Accountability and Focus: By establishing clear pathways for impact, AuRA enhances accountability and ensures resources are directed towards the most promising solutions.
In a recent study on women’s economic empowerment, researchers used AuRA to evaluate various interventions aimed at enhancing financial inclusion for women in rural areas. AuRA swiftly compiled a comprehensive data library, mapped out the impact pathways, and identified the most promising interventions. This enabled the research team to make swift, data-driven decisions with respect to which initiatives to pilot, accelerating the implementation of solutions that significantly improved the financial independence of hundreds of women.
FINCA’s Automated Research Assistant (AuRA) is a testament to the transformative power of customized AI solutions in social impact research. By expediting the path to knowledge, AuRA not only enhances decision-making but also fortifies FINCA’s commitment to combating poverty and its intertwined challenges. This bespoke AI-driven solution exemplifies how tailored technology can drive meaningful change in social impact initiatives.
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