Resources

A state news organization collaborated with the Institute for Experiential AI to develop a comprehensive AI and Data Opportunity Roadmap supporting its digital transformation strategy. Through stakeholder discovery, AI maturity assessment, and structured opportunity prioritization, the project identified high-impact AI use cases across content creation, audience engagement, advertising, and operational efficiency. The engagement delivered a clear, actionable roadmap to guide responsible AI adoption while strengthening trust, sustainability, and innovation in local journalism.

A technology company collaborated with the Institute for Experiential AI to develop a privacy-first AI transcription analysis engine and explore future AI service opportunities built on its Solid personal data framework. The project combined large language model–based summarization and sentiment analysis pipelines with structured ideation and design workshops to identify high-impact, privacy-preserving AI services across multiple domains. By integrating applied AI experimentation with strategic service design, the engagement advanced responsible, user-owned data innovation within decentralized ecosystems.

An independent nonprofit collaborated with the Institute for Experiential AI to assess the feasibility of using AI to extract meaningful insights and patterns from interview transcripts with social innovators. The project explored natural language processing and summarization techniques to identify recurring lessons, distinctive approaches, and transferable insights across interviews. The engagement delivered a validated prototype approach and clear guidance for scaling AI-driven insight extraction to support impact-focused journalism and knowledge sharing.

An AI-enabled credit fund platform company collaborated with the Institute for Experiential AI to design a predictive AI model that forecasts the probability of illiquidity events in private equity firms. The project applies interpretable machine-learning techniques to financial, operational, and macroeconomic data, enabling risk-aware liquidity scoring for institutional and individual investors. By prioritizing model transparency and rigorous data assessment, the engagement laid the foundation for a scalable liquidity risk bureau for private markets.

A promotional products supplier collaborated with the Institute for Experiential AI to design a comprehensive Data Opportunities Roadmap supporting its digital and AI transformation journey. Through executive AI education, cross-functional discovery workshops, and structured opportunity ranking, the project identified and prioritized high-impact AI, analytics, and automation initiatives aligned with their strategic goals. The engagement delivered a clear, actionable roadmap to guide future innovation and data-driven value creation across the organization.

A global insights platform worked with the AI Solutions Hub to design and build a bespoke machine-learning pipeline that predicts the likelihood of project success based on historical project, expert, and client requirement data. The solution integrates large-scale structured and unstructured datasets, applies advanced predictive modelling, and embeds responsible AI practices throughout the pipeline. By enabling more accurate project prioritization and outcome forecasting, the system supports faster, data-driven decision-making across Atheneum’s global research platform.

MaineHealth collaborated with the Institute for Experiential AI to design an AI-driven scheduling optimization solution for simulation events at its Simulation Center. The project addressed highly complex, manual scheduling processes by centralizing data in a structured database and applying constraint satisfaction optimization to generate feasible and efficient schedules. By automating scheduling workflows through a web-based interface, the solution reduces operational risk, dependency on manual effort, and scheduling errors while improving resource utilization.

The Cradle-to-Prison Pipeline (C2P) project worked with the AI Solutions Hub to design an end-to-end data processing and analytics framework supporting justice and social impact advocacy. The initiative focused on consolidating arrest report data from across Massachusetts, developing interactive geospatial visualizations, and designing scalable architecture for future analytics expansion. By combining data engineering, exploratory analysis, and human-centered analytics, the project enables advocates to better understand structural contributors to incarceration and supports evidence-based policy action.

An integrated environmental services company partnered with Northeastern University’s Institute for Experiential AI to develop an AI-enabled evaluation and ROI framework for a data science–driven water and energy optimization product. The engagement combined operational analysis, data science model review, and financial modeling to assess product impact across multiple facilities. The solution enabled evidence-based decision-making for large-scale infrastructure deployment and future operational scaling.

An AI-powered learning and capability platform collaborated with the Institute for Experiential AI to design and deliver a custom, end-to-end assessment grading engine that automates the evaluation of technical interview responses. Leveraging large language models, machine learning, and dynamically generated grading rubrics, the solution enables consistent, scalable, and responsible scoring of transcribed video interviews. By integrating automated grading directly into recruitment workflows, the project significantly reduces manual effort while improving fairness and reliability in technical hiring decisions.

A workers' compensation insurance company collaborated with the AI Solutions Hub to develop an AI-driven Claims Complexity Scoring Engine and an Intelligent Claims Routing Engine to modernize its claims intake process. Using historical claims data, the solution assigns complexity scores to incoming claims and automatically routes them to the appropriate handlers, improving efficiency, consistency, and risk management. The project integrates responsible AI assessment, scalable cloud infrastructure, and operational handover to support long-term deployment within MEMIC’s claims operations.

The Maine Cancer Genomics Initiative collaborated with the AI Solutions Hub to develop an AI-powered solution that automates complex genomic data extraction from testing reports and structures tumor board transcripts into queryable formats. By reducing manual data processing and summarisation time, the solution enables faster, more accurate clinical decision-making and supports improved care for cancer patients.

A global digital transformation and IT services company worked with the AI Solutions Hub to design and build a custom, end-to-end AI-powered candidate matching system for healthcare staffing. The solution automates resume parsing, deduplication, and multi-criteria candidate-to-job matching, incorporating state licensing, certifications, experience, and implicit recruiter knowledge. Through feedback-driven learning and scalable infrastructure, the system enables rapid, accurate staffing decisions while reducing manual workload and strengthening Cambay’s competitive position in the healthcare staffing market.

A forest products company collaborated with the AI Solutions Hub to design and deploy a proof-of-concept AI chatbot that enables employees to query internal documentation such as HR policies, safety procedures, and operational manuals. Built using a Retrieval-Augmented Generation (RAG) architecture within the Microsoft 365 ecosystem, the solution delivers accurate, traceable answers directly sourced from company documents. The project demonstrated how enterprise-grade generative AI can improve internal knowledge access while maintaining compliance, provenance, and security.

A global executive search firm collaborated with the Institute for Experiential AI to design and build a custom AI-powered interview summarization engine that transforms raw interview transcripts into high-quality, client-ready summaries. The solution combines fine-tuned large language models with human-in-the-loop review, continuous learning pipelines, and quality evaluation metrics to ensure consistency, accuracy, and scalability. By automating interview summarization while preserving consultant oversight, the project significantly reduces manual effort and improves speed and quality across executive search workflows.

A health technology company collaborated with the Institute for Experiential AI to design and deploy a custom, end-to-end retrieval-augmented generation (RAG) platform supporting non-clinical assessment of bone, joint, and muscle health. The solution integrates large-scale PubMed ingestion, document processing, vector search, and human-in-the-loop reinforcement learning from expert feedback. Deployed on scalable AWS infrastructure, the system enables domain-specific long-form medical question answering while maintaining strict performance, transparency, and responsible AI standards.

A property management company worked with the AI Solutions Hub to develop an AI-powered reporting platform that automates data extraction from RealPage across more than 37,000 housing units. The solution uses large language models to translate natural language questions into SQL queries and delivers on-demand insights through a conversational web interface. By eliminating hours of manual Excel-based reporting, the platform significantly improved operational efficiency and decision-making speed.

A research assistant tool company developed an AI-powered customer response assistant designed to support users by answering questions drawn from community forums and technical documentation. Built using a retrieval-augmented generation (RAG) architecture, the solution combines curated knowledge sources with large language models to deliver accurate, context-aware responses. The engagement emphasized responsible AI practices, including data governance, evaluation, and human-in-the-loop validation, to ensure reliability and scalability for a global user base.

An infrastructure services provider collaborated with the AI Solutions Hub to develop AIPEX, a bespoke, cloud-based invoice processing solution that automates data extraction from more than 300 weekly PDF invoices. The system reduces manual data entry and improves accuracy for project managers, streamlining financial workflows and enhancing operational efficiency across infrastructure projects.

A health technology company partnered with the Institute for Experiential AI to design a comprehensive Data Opportunity Roadmap supporting its long-term AI and analytics transformation. The engagement combined stakeholder discovery, data capability assessment, and structured opportunity prioritization to identify high-impact AI initiatives aligned with their mission to improve healthcare affordability and outcomes. The project delivered a clear, actionable roadmap to guide scalable, responsible AI adoption across the organization.

A nonprofit provider supporting adults with various disabilities collaborated with the Institute for Experiential AI to develop a comprehensive Data Opportunity Roadmap aimed at improving service delivery, operational efficiency, and compliance through data and AI. The engagement assessed their data, analytics, and AI maturity across people, processes, and technology, and identified high-impact opportunities aligned with its mission and growth objectives. The project delivered a prioritized, actionable roadmap to guide AI and analytics investments over the next 12–24 months.

A utility company collaborated with the Institute for Experiential AI to develop a comprehensive Data Opportunity Roadmap supporting its long-term digital and operational transformation. Through stakeholder discovery, data and analytics maturity assessment, and structured opportunity prioritization, the project identified high-impact AI use cases across forecasting, resource optimization, anomaly detection, and decision support. The engagement delivered a clear, actionable roadmap to guide responsible AI adoption while strengthening operational resilience and environmental stewardship.

The HEA(RT) Impact Engine is a multi-year AI innovation initiative designed to reduce adverse events and costs for patients recovering from cardiothoracic surgery in intensive care settings. Led by Northeastern University faculty across engineering, health sciences, and life sciences, the project applies real-time AI models to clinical data streams, including EMR and vital signs, to improve early risk detection and clinical decision support. By integrating advanced machine learning with human-centered clinical workflows, HEA(RT) advances scalable, responsible AI innovation in critical care medicine.

An insurance company project assessed fairness, bias, and ethical risks in an AI system used to predict long-term disability claim resolution. Northeastern University’s Institute for Experiential AI conducted a comprehensive bias audit, including stakeholder interviews, data analysis, and group- and individual-level fairness evaluation across demographic and socio-economic factors. The engagement delivered actionable recommendations for bias mitigation, ongoing monitoring, and the development of a robust Responsible AI and data governance framework.

A private equity company collaborated with the Institute for Experiential AI to design and deliver the Labor Market Indicator (LMI), a data-driven tool for analyzing labor market dynamics and wage trends. The project combined structured economic data, analytical modeling, and visualization to surface insights on employment patterns and workforce conditions. Delivered through a phased engagement, the LMI supports evidence-based decision-making for impact-focused investment and policy initiatives.

The Jackson Laboratory collaborated with the Institute for Experiential AI to develop an advanced demand forecasting and latent demand analysis framework to better align colony production with global customer demand. The project combined machine learning–based demand forecasting with the extraction of latent demand signals from CRM data, call transcripts, and supply constraints to quantify unmet customer needs. By integrating latent demand into forecasting models, the solution enabled more accurate, forward-looking planning and improved operational decision-making across JAX’s production and sales workflows.

A technology company joined forces with with the AI Solutions Hub to develop a new class of privacy-first generative AI applications built on its Solid architecture, where users retain full ownership and control of their data. The project integrated large language models with decentralized data wallets, enabling highly personalized AI experiences without centralized data collection or model retraining on user data. The work demonstrated a scalable, responsible approach to generative AI and paved the way for their Active Wallet platform across multiple industries.

A professional services firm engaged with the AI Solutions Hub to develop RADAR, an AI-powered compliance system that ingests multi-document contracts and automatically extracts, structures, and aligns regulatory requirements. Using machine learning to map requirements to organizational controls, the solution identifies compliance gaps with high accuracy and streamlines contract analysis for clients operating in highly regulated industries.

RadioLifeLabs collaborated with the AI Solutions Hub to develop AI models that leverage radio-frequency sensor technology for the multiplex detection of Dengue, Chikungunya, and Oropouche viruses. The solution applies machine learning to identify pathogens and determine minimum detectable concentrations, with the potential to significantly accelerate diagnostic testing. By combining novel sensing technology with AI, the project advances faster, more accessible diagnostics in infectious disease detection.

A financial services firm engaged the Institute for Experiential AI for a six-month Responsible AI advisory engagement focused on strengthening AI ethics and governance practices across the enterprise. The project provided expert guidance on ethical risk assessment, data governance, bias mitigation, and explainability, supporting the design of a scalable framework for an external Ethics Advisory Board. Through ongoing executive advisory sessions, the engagement helped them operationalize Responsible AI principles in a rapidly evolving regulatory and technological landscape.

A major commercial bank partnered with the Institute for Experiential AI to develop a proof-of-concept dynamic credit limit model for small business credit card customers. The AI-driven solution leverages historical transaction data and behavioral signals to adjust credit limits proactively, supporting customer growth while mitigating credit risk. By combining responsible AI practices with data-driven decisioning, the project laid the foundation for scalable, adaptive credit management across their lending portfolio.

A global IT services and consulting company engaged with Northeastern University’s Institute for Experiential AI and the AI Solutions Hub in a strategic, multi-day innovation engagement focused on applied AI across healthcare, life sciences, digital engineering, and enterprise transformation. The engagement brought together their executive leadership, AI practitioners, and Northeastern faculty to explore real-world AI use cases, delivery capabilities, talent development, and responsible AI implementation. The visit established a foundation for long-term collaboration, co-innovation, and joint industry-facing AI initiatives.

An insurance company teamed up with the AI Solutions Hub to develop an AI-driven underwriting and fraud detection solution that automates document validation and risk classification. The system reduces manual underwriting intervention while improving fraud detection accuracy and overall operational efficiency. By embedding AI into core underwriting workflows, the project strengthened risk management and enabled faster, more reliable decision-making.