This project was not about scraping for the sake of scraping. It was about creating a VIN-level service history and logbook intelligence layer — internal decision support first, B2B infrastructure next, and consumer trust outputs later.
Used-car buyers, sellers, dealers, and internal Cars24 teams do not have a simple, trusted way to verify whether a vehicle has been serviced properly, whether the logbook is complete, and whether service records align with odometer, recall, and inspection signals.
Cars24 can seed the database before partnerships scale it, using internal inspection data, marketplace odometer history, recall infrastructure, and uploaded service records.
This becomes a B2B data infrastructure layer for dealers, lenders, warranty providers, finance, and service partners — not just a consumer feature.
| Layer | Meaning | Example source |
|---|---|---|
| Logbook record | Scheduled maintenance entry | Uploaded service book or dealer record |
| Service invoice | Workshop/dealer invoice | Customer upload or service-centre export |
| OEM/dealer service | Authorised service record | Dealer networks / OEM systems |
| Recall service action | Evidence recall repair was completed | OEM/dealer confirmation |
| Odometer at service | KM reading at service event | Invoice, logbook, inspection, listing |
| Service consistency | Whether history is recent and complete | Calculated confidence logic |
The scraping / discovery piece existed because service records are fragmented across OEM systems, dealer DMS tools, workshop software, uploaded documents, and Cars24's own internal layers. Mapping those sources was part of building the service-history foundation.
| Priority | Source | Data provided | Why it matters |
|---|---|---|---|
| P0 | Cars24 inspection data | Condition, odometer, defects, photos | Already internal and immediately usable. |
| P0 | Customer-uploaded logbooks / invoices | Service evidence | Fastest way to seed the VIN timeline. |
| P0 | Recall-check data | Recall campaigns and VIN match | Useful matching layer for service completion evidence. |
| P1 | Workshop / service-centre discovery | DMS list, exportability, workflow reality | Turns theory into a scalable ingestion path. |
| P2 | OEM / dealer partnerships | Authorised service records | High-value but harder to unlock. |
Interview OEM, dealer, and workshop stakeholders; validate service workflows; map DMS tools; review current CHR output; and document what data is realistically accessible.
Output: source map, access assumptions, shortlist, and open questions.Create a VIN-level structure for service and logbook evidence before any public product. Define service-event schema, confidence rules, upload fields, and recall-service matching logic.
Output: internal repository prototype for buy, pricing, and inspection support.Use OCR, manual review, and structured extraction to turn uploaded logbooks, invoices, recall certificates, and notes into normalized service records.
Output: structured service timeline with confidence labels.Pilot with service centres or DMS providers, then package the repository into dealer, lender, warranty, and partner outputs once data quality and economics hold.
Output: partner-facing service-history intelligence layer.The deeper walkthrough can still live at /ai-automation/service-history-scraper/demo, but the main page now reflects the real strategic scope behind the project.