Edmunds automates dealer review moderation with GPT-4 via Databricks Model Serving
Edmunds received over 300 dealer quality-of-service reviews daily that were moderated manually, sometimes taking up to 72 hours before vetted reviews could be published. IAM-based data governance also created coarse access control and little visibility into pipeline dependencies.
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 · Daily review submissions
Over 300 dealer quality-of-service reviews are submitted daily on Edmunds.
Tools used
DatabricksDatabricks Model ServingGPT-4
Outcome
Edmunds now auto-moderates dealer reviews in minutes instead of up to 72 hours, saving three to five hours of staff time per week and requiring only two moderators freed for higher-value tasks. Unity Catalog migration delivered improved auditing, compliance, security, and data discovery.
What failed first
Training an off-the-shelf model produced poor results, and fine-tuning still failed to handle the complex moderation rules because all rules had to be captured in the prompt with no flexibility for edge cases. Prompt engineering experiments were also difficult because there was no easy way to compare outputs across different models.