BIDBAJ
Enter a VIN, get every valuation source, the window sticker, and the number you should actually bid — in both currencies.
A dealer deciding what to bid on a used vehicle has to consult several valuation sources — Kelley Blue Book, J.D. Power, Manheim Market Report, Black Book, auction comps — each behind its own login and interface, then find the original window sticker to know the vehicle's real specification and MSRP, then subtract reconditioning and pack costs, then work out what that means as a percentage of MSRP. For a dealer buying across the US–Canada border there's a further conversion step, and the answer has to come out in both currencies. Done manually that's twenty minutes per vehicle, at a point in the process where dealers are looking at dozens of cars and have minutes to decide.
A Django platform that collapses the appraisal into one VIN lookup, with an automation layer doing the collection. A bot drives the valuation sources and window-sticker retrieval in a real browser session, pulls the sticker PDF, and extracts its content — using OCR and NLP rather than assuming a parseable layout, since window stickers are manufacturer-designed documents that differ by brand. Fuzzy matching reconciles the extracted specification against the valuation records. Each VIN carries mileage and an appraisal holding every source's figure side by side, an itemised expense breakdown with a default pack cost, MSRP from the sticker, and the calculated opening and ceiling bids in both USD and CAD with the conversion rate captured at the time, plus each bid as a percentage of MSRP — which is the ratio dealers actually judge a deal by. VINs are flagged as appraised and as closed once a deal is done. Alongside the appraisal core sits dealer-to-dealer messaging with reporting and blocking, and registration is invitation-gated rather than open, so the platform stays a trade tool.
Outcome
A dealer appraisal tool built as four parts — Django backend, React interface, Flutter mobile app, and the automation bot — turning a multi-source manual valuation into a single VIN entry that returns every reference price, the original window sticker, itemised costs, and a defensible bid range in both currencies
Key features
- Single VIN entry returning all valuation sources — KBB, J.D. Power, MMR, Black Book, auction and KBB.com
- Automated collection driving the valuation sources in a real browser session
- Original window sticker retrieval, with OCR and NLP extraction across differing manufacturer layouts
- Fuzzy matching reconciling extracted specification against valuation records
- Itemised expense breakdown with a default pack cost
- Opening and ceiling bid calculation in both USD and CAD, with the conversion rate captured
- Each bid expressed as a percentage of MSRP
- Mileage-adjusted appraisal per VIN
- Appraised and deal-closed flags per VIN
- Dealer-to-dealer messaging with reporting and blocking
- Invitation-gated registration
- Four clients on one system — Django backend, React web, Flutter mobile, automation bot
Tech stack
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