Project notes
Repair Trends helps technicians identify components likely to fail and cause an unscheduled repair within a requested mileage range. The upstream repair-order data had coverage gaps and could include scheduled-wear or collision-related replacements.
I independently built and deployed the AI service using FastAPI on Cloud Run and Gemini through Vertex AI. Teammates owned the upstream cleaning pipeline and querying API.
The service uses aggregated repair-order observations as grounding to rerank components, revise probabilities, and suggest missing components. It accounts for scheduled-wear and collision-related biases and generates suggestions when the upstream component list is empty.
An AlloyDB cache reuses responses for repeated vehicle, mileage, and component inputs. When the upstream component list changes, the service regenerates and replaces the cached response.
Direct data covered 80 of 100 expert-selected vehicle and mileage test cases. The service produced AI-assisted output for all 100 when available, including the 20 without underlying observations. Subject-matter experts reviewed every case. This is test-set coverage, not an accuracy measurement.
The beta/staging rollout is complete as of October 2026.