compliance_monitoring · media · workflow
Philadelphia Union builds GenAI RAG chatbot for MLS roster management on Databricks
MLS Roster Composition Rules and Regulations are extensive and filled with legalistic details, slowing down the Philadelphia Union front office's decision-making during roster planning and transfer market activity.
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 · Front office submits query
The chatbot is accessed through a no-code, ChatGPT-like interface deployed via Databricks Apps.
Tools used
Databricks Data Intelligence PlatformDatabricks AppsRAGVector SearchDatabricks VolumesDatabricks Foundation Model APIDBRX InstructAgent Bricks Custom Agents
Outcome
The GenAI chatbot enables zero-shot interpretation of roster regulations in seconds, accelerating decision-making and allowing the front office to focus on strategic tasks while maintaining compliance with MLS guidelines.
Results
Time savedsaves valuable time
Grounding & classification
Source type: technical build writeup
28 fields verified against source quotes.
chatbotconversational aiknowledge searchragpolicy documenthuman review describednamed customerproduction runtime claimedtools describedworkflow describedmediacycle time reductionemployee productivitytechnical build writeupback office opscompliance monitoringrag answering