Crisis Text Line uses Databricks to centralize data and deploy LLM-powered crisis counselor training
Crisis Text Line had a fragmented, siloed data infrastructure where business rules and context were lost across silos, preventing downstream teams from making efficient real-time decisions as the organization scaled to support over 1.3 million conversations per year.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Data centralization to single source
Databricks enabled Crisis Text Line to centralize its data and create a single source of truth.
Databricks enabled Crisis Text Line to centralize data into a single source of truth, dramatically reduce dataset availability time for clinical teams, deploy an LLM-powered counselor training simulator used by over 50 volunteers and clinicians, and reduce infrastructure overhead while improving program ROI.
What failed first
Legacy infrastructure suffered from query timeouts, batch-script failure points, and siloed data that led to duplicated efforts and an inability to build a cohesive data culture across the organization.