## Overview Crisis Text Line is a nonprofit organization that provides free, 24/7, confidential, text-based mental health support and crisis intervention services. Operating in the United States and internationally through affiliates in Canada, the UK, and Ireland, the organization has facilitated over 1.3 million crisis intervention conversations in the past year alone, supported by approximately 100,000 trained volunteers globally. The organization's mission extends beyond individual crisis support to leveraging operational insights for public policy influence and systemic change in mental health care. This case study demonstrates how Crisis Text Line transformed their data infrastructure and implemented generative AI solutions to improve volunteer crisis counselor training and operational efficiency. While the case study is presented through a Databricks marketing lens, it provides valuable insights into practical LLMOps implementation for a mission-critical mental health application. ## The Problem: Fragmented Data Infrastructure Before their technology transformation, Crisis Text Line struggled with a siloed and fragmented data landscape that created several operational challenges. Their legacy infrastructure suffered from inconsistent business rules and context that were often lost across data silos, making it difficult for downstream operational teams to make efficient real-time decisions. Simple database queries frequently timed out, and the batch scripts used for data transformation created multiple points of failure. This fragmented approach presented particular challenges for an organization handling sensitive mental health data. Without a single source of truth, it was difficult to build a cohesive data culture across the organization, leading to duplicated efforts across teams. The organization required a modern technology stack that could support agile development, quick iteration, better collaboration, and crucially, strong governance and security given the sensitive nature of crisis counseling conversations. ## The Solution: Centralized Data Platform with GenAI Capabilities Crisis Text Line adopted the Databricks Data Intelligence Platform to centralize their data operations and create a federated data store. The platform addressed their core infrastructure challenges while enabling advanced AI capabilities. ### Data Infrastructure and Governance The implementation of Unity Catalog proved instrumental in managing data access at granular table and column levels. This was particularly important given the sensitive nature of mental health crisis data, allowing different data profiles to maintain appropriate access controls while enabling teams to move quickly with proper oversight. The organization can now ingest and process data in a more reliable and secure manner, dramatically reducing the time required to make critical datasets available for clinical teams, analytics groups, and machine learning work. ### MLOps Tooling The organization leverages several Databricks components for their data and AI workflows. MLflow serves as their central system for managing the full model lifecycle, from training to deployment. It handles experimentation tracking and model versioning, ensuring that the correct models are released in appropriate capacities across different workspaces. This is a critical LLMOps consideration when dealing with multiple model versions and deployment environments. Spark Declarative Pipelines (previously known as Delta Live Tables) supports their post-processing pipeline infrastructure. This capability enables data scientists to define business entities without requiring dedicated data engineering support, accelerating iteration and production deployment processes. Databricks Notebooks facilitate cross-functional collaboration, active troubleshooting, and rapid experimentation within a secure and compliant environment. ## Generative AI Use Cases The case study highlights two significant generative AI applications that demonstrate practical LLMOps in action: ### LLM-Powered Conversation Simulator The primary generative AI use case is a conversation simulator that allows new crisis counselors to practice engagement strategies in difficult scenarios. This training tool enables volunteers to build confidence without real-world repercussions, addressing a fundamental challenge in crisis counselor training: the need for realistic practice without risking harm to actual individuals in crisis. The technical implementation involved fine-tuning Llama 2 models using synthetic conversation role-plays created by Crisis Text Line's clinical staff as an initial training dataset. This approach is noteworthy from an LLMOps perspective for several reasons. First, using synthetic data created by domain experts (clinical staff) ensures the training conversations reflect realistic crisis scenarios while avoiding the privacy and ethical concerns of using actual crisis conversations for training purposes. Second, fine-tuning an open-source model like Llama 2 rather than relying on external API services keeps sensitive data within their controlled infrastructure, addressing security and compliance requirements. The conversation simulator has been deployed and used by over 50 real volunteers and clinicians to enhance their training and readiness. While this is a relatively modest deployment scale, it represents a production use case with real users and measurable impact on volunteer training quality. ### Conversation Phase Classifier A second AI system under development is a conversation phase classifier designed to help the team assess conversation quality and ensure appropriate responses. While fewer details are provided about this system, it appears to be aimed at quality assurance and monitoring of crisis counseling conversations. This type of classifier could help identify when conversations are progressing appropriately through expected phases (initial contact, assessment, intervention, closing) and flag cases that may need supervisory attention. ## LLMOps Considerations and Best Practices Several LLMOps best practices emerge from this case study: ### Model Lifecycle Management The use of MLflow for managing the full model lifecycle demonstrates a mature approach to model governance. Tracking experimentation, versioning models, and managing deployments across workspaces are fundamental LLMOps capabilities that enable reproducibility and controlled rollouts. For an organization dealing with mental health interventions, the ability to track which model version is deployed and rollback if issues emerge is particularly important. ### Secure Data Handling for Fine-Tuning The approach of using clinically-created synthetic data for fine-tuning represents a thoughtful solution to the challenge of training domain-specific models on sensitive data. Rather than exposing actual crisis conversations to training pipelines (with associated privacy and consent concerns), the clinical staff created representative scenarios that capture the patterns and challenges of crisis counseling without using real user data. ### Governance and Access Control Unity Catalog's granular access controls at table and column levels enable a principle of least privilege approach to data access. This is essential for healthcare-adjacent applications where different team members (data scientists, clinical staff, researchers) may have legitimate needs for different data subsets, but blanket access to all data would be inappropriate. ### Collaboration Infrastructure The emphasis on collaborative notebooks and tools that enable cross-functional teams to work together reflects the reality that LLMOps in production requires coordination between data engineers, data scientists, clinical domain experts, and operational teams. The ability to prototype, troubleshoot, and iterate quickly within a shared environment accelerates the path from research to production. ## Results and Impact The organization reports several concrete outcomes from their technology transformation. The centralized data platform now supports all reporting needs for launching and scaling crisis interventions, with data product dashboards monitoring program health and quality. They cite improvements in user adoption of dashboard artifacts, increased program ROI as customers and affiliates gained trust in operational and analytical expertise, and reduced infrastructure and engineering overhead. The conversation simulator represents a tangible AI-powered improvement to volunteer training, though the case study does not provide detailed metrics on training effectiveness or time-to-proficiency improvements. ## Critical Assessment While this case study presents a compelling narrative of digital transformation and AI adoption, several caveats are worth noting. The case study is presented through Databricks' marketing materials, which naturally emphasizes the platform's benefits. The quantitative metrics provided (1.3 million conversations, 100,000 volunteers) describe overall organizational scale rather than specific improvements attributable to the technology changes. The conversation simulator deployment to "over 50 real volunteers and clinicians" is a relatively small pilot, and the case study does not provide evidence of efficacy in improving counselor performance or client outcomes. For a mission-critical mental health application, such validation would be important before broader deployment. Additionally, while the case study mentions that a conversation phase classifier is "under development," it does not indicate whether this has reached production or demonstrated value yet. Despite these caveats, the case study illustrates a realistic and responsible approach to implementing LLMs in a sensitive healthcare-adjacent domain, with appropriate attention to data governance, security, and the use of synthetic training data to protect privacy.