case studies

Building a private LLM sandbox for NCSC

We worked with the National Center for State Courts to build an LLM chat sandbox for private usage.

NCSC LLM sandbox

The organization

The National Center for State Courts (NCSC) is a non-profit organization that provides research, training, and technical assistance to state courts. Through their work NCSC improves how courts operate and how they serve the public, bringing together best practices and technology to help courts operate more efficiently and effectively.

The challenge

NCSC wanted to build a private LLM sandbox that would enable state court staff to begin experimenting with LLM tools in a way that was provider agnostic and guaranteed ensured robust data privacy. In addition to controlling the data and models that are available, NCSC wanted to provide a library of specific, court-targeted use cases as starting points for chats. This custom interface would allow experimentation with the latest state-of-the-art LLMs in a way that surfaced the most relevant applications to their user base.

The approach

DrivenData developed and deployed a custom chat interface for NCSC. Users can create accounts, select from a list of state-of-the-art LLMs, and chat with the model. In addtion to normal chat flows, we also provided a document upload functionality that would enable users to chat with documents they uploaded for use cases like summarizing proceedings or finding specific information in a law or regulation.

NCSC LLM prompt library
The pre-built prompts in the NCSC LLM prompt library that demonstrate specific use cases.

The application was built using typescript, react, and next.js. The backend uses a range of Microsoft Azure services, including Azure OpenAI, Azure App Service, and Azure Cosmos DB. LLMs were provided via the the Azure OpenAI API, which provides enterprise-grade data privacy and compliance guarantees. The system allows simple monitoring of usage, costs, and user management to NCSC while keeping operating costs low. New models and prompts for the use-case library are easily added to expand what the sandbox can offer to users.

The results

NCSC makes the deployed production system available to users and uses it as part of their training and research. The system enables state court staff to experiment with LLMs in a way that is safe, secure, and cost-effective. Ultimately, this can help courts understand the capabilities of LLMs and how they can be used to improve their operations.

As NCSC put it:

DrivenData has been a great partner in our work. Our goal was to set up an AI sandbox. The DrivenData team helped us to define our specific needs, prioritize them, create tools to meet those needs, and set up data collection to evaluate the performance. Their communication was timely and professional across all areas. They completed the work on time and within budget. I would be delighted to work with them again.

Diane Robinson, Principal Court Research Associate, National Center for State Courts

Learn more about the National Center for State Courts and their work at ncsc.org.

Our real-world impact

All projects
Partners: CodePath

Data engineering from the ground up

Built data infrastructure to ingest, clean, integrate, and organize data across CodePath, created interactive dashboards for accurate monitoring of program trends, and provided trusted data expertise to identify and hire talent to carry the work forward.

Partners: Max Planck Institute for Evolutionary Anthropology, Arcus Foundation, WILDLABS

Automating wildlife identification for research and conservation

Detected wildlife in images and videos—automatically and at scale—by building the winning algorithm from a DrivenData competition into an open source python package and a web application running models in the cloud.

Partners: Private sector, social sector

Building LLM solutions

Built solutions using LLMs for multiple real-world applications, across tasks including semantic search, summarization, named entity recognition, and multimodal analysis. Work has spanned research on state-of-the-art models tuned for specific use cases to production ready retrieval-augmented AI applications.

Partners: The World Bank, The Conflict and Environment Observatory

Identifying crop types using satellite imagery in Yemen

Used satellite imagery to identify crop extent, crop types and climate risks to agriculture in Yemen, informing World Bank development programs in the country after years of civil war.

Partners: Bureau of Ocean Energy Management, NOAA Fisheries, Wild Me

Protecting endangered beluga whales with computer vision

Designed and administered a computer vision challenge that produced state-of-the-art machine learning models to identify and match individual endangered beluga whales from photo surveys.

Partners: EverFree

A production application to support survivors of human trafficking

Built the Freedom Lifemap platform, a digital tool designed to support survivors of human trafficking on their journey toward reintegration and independence

Partners: ReadNet

Crowdsourcing solutions for AI assisted early literacy screening

Ran a machine learning challenge to develop automatic scoring methods for audio clips from literacy screener exercises. Automated scoring can help teachers quickly and reliably identify children in need of early literacy intervention.

Partners: Science for America

Making higher education data more accessible

Created an open source Python library and interactive data visualization platform for analyzing U.S. higher education data and illuminating trends and disparities in STEM education.

Partners: IDEO.org

Illuminating mobile money experiences in Tanzania

Analyzed millions of mobile money records to uncover patterns in behavior, and then combined these insights with human-centered design to shape new approaches to delivering mobile money to low-income populations in Tanzania.

Partners: Insecurity Insight, Physicians for Human Rights

Tracking attacks on health care in Ukraine

Built a real-time, interactive map to visualize attacks on the Ukrainian health care system since the Russian invasion began in February of 2022. The map will support partner efforts to provide aid, hold aggressors accountable in court, and increase public awareness.

Partners: Wellcome

Addressing algorithmic bias in medical research

Conducted a literature review to understand the current state of bias identification & mitigation in mental health research, and synthesized recommended best practices from the field of machine learning.

Partners: CABI Plantwise

Mining chat messages with plant doctors using language models

Automated recognition of agricultural entities (such as crops, pests, diseases, and chemicals) in WhatsApp and Telegram messages among plant doctors, enabling new ways to surface emerging trends and improve science-based guidance for smallholder farmers.

Partners: NASA

Monitoring water quality from satellite imagery

Created an open-source package to detect harmful algal blooms using machine learning and satellite imagery. Included running a machine-learning competition, conducting end user interviews, and engineering a robust, deployable pipeline.

Partners: Data science company foundation

Matching students with schools where they are likely to succeed

Used machine learning to match students with higher education programs where they are more likely to get in and graduate based on their unique profile, with a focus on backgrounds traditionally less likely to attend college or apply to more competitive programs.

Partners: Fair Trade USA

Mapping fair trade products from source to shelf

Visualized the flow of fair trade coffee products from the farms where they are grown to the stores where they are sold, connecting the nodes in supply chain transactions and increasing transparency for customers and auditors.

Partners: University of Maryland

Processing multimodal tutoring data

Built well-engineered data pipelines to extract machine learning features from audio, video and transcript data collected from online tutoring sessions, enabling a team at the University of Maryland to study how relationship-building affects student outcomes.

Partners: The World Bank, Angaza, GOGLA, Lighting Global

Developing performance indicators and repayment models in off-grid solar

Analyzed repayment behaviors across dozens of pay-as-you-go (PAYG) solar energy companies serving off-grid populations throughout Africa, and developed KPIs to facilitate standardized reporting for PAYG portfolios.

Partners: Haystack Informatics

Modeling patient pathways through hospitals

Mapped out the probabilistic patient journeys through hospitals based on tens of thousands of patient experiences, giving hospitals a better view into the timing of the activities in their departments and how they relate to operational efficiency.

Partners: Yelp, Harvard University, City of Boston

Predicting public health risks from restaurant reviews

Flagged public health risks at restaurants by combining Yelp reviews with open city data on past inspections. An algorithmic approach discovers 25% more violations with the same number of inspections.

Partners: Education Resource Strategies

Smart auto-tagging of K-12 school spending

Built algorithms that put apples-to-apples labels on school budget line items so that districts understand how their spending stacks up and where they can improve, saving months of manual processing each year.

Work with us to build a better world

Learn more about how our team is bringing the transformative power of data science and AI to organizations tackling the world's biggest challenges.