Custom AI Vehicle Diagnostics Solution
Budget: $1,500 – $3,000 USD
I need a bespoke AI model that zeroes in on vehicle diagnostics—this is the core objective, rather than predictive maintenance, logistics, or customer-support use cases. The diagnostic scope I have in mind sits outside the usual engine-, transmission-, or emissions-centric checks, so I’m looking for inventive thinking around fault detection, anomaly classification, and root-cause analysis that can be adapted to my specific fleet and service workflow.
Data is not a blocker: I can provide continuous on-board diagnostics (OBD) streams, high-frequency sensor feeds, and detailed maintenance records. You’ll help shape a pipeline that ingests, cleans, and labels this information, then train and validate a model (or ensemble) that can surface actionable insights in near-real time.
Preferred stack is flexible—Python, TensorFlow or PyTorch for modelling, and whatever mix of cloud or edge deployment makes sense—but the final deliverable must expose a well-documented API plus a lightweight dashboard so my technicians can verify findings without wading through raw logs.
Key acceptance criteria
• End-to-end pipeline from data import to inference runs on sample vehicle data
• Model accuracy and false-positive rates benchmarked against an agreed test set
• API endpoints documented (OpenAPI/Swagger) and authenticated
• Simple web UI or Grafana panel showing live diagnostics and alert history
If you have prior wins with OBD data, sensor fusion, or similar automotive AI projects, let’s talk details and timeline.
Data is not a blocker: I can provide continuous on-board diagnostics (OBD) streams, high-frequency sensor feeds, and detailed maintenance records. You’ll help shape a pipeline that ingests, cleans, and labels this information, then train and validate a model (or ensemble) that can surface actionable insights in near-real time.
Preferred stack is flexible—Python, TensorFlow or PyTorch for modelling, and whatever mix of cloud or edge deployment makes sense—but the final deliverable must expose a well-documented API plus a lightweight dashboard so my technicians can verify findings without wading through raw logs.
Key acceptance criteria
• End-to-end pipeline from data import to inference runs on sample vehicle data
• Model accuracy and false-positive rates benchmarked against an agreed test set
• API endpoints documented (OpenAPI/Swagger) and authenticated
• Simple web UI or Grafana panel showing live diagnostics and alert history
If you have prior wins with OBD data, sensor fusion, or similar automotive AI projects, let’s talk details and timeline.