Vehicle-wrap workflow SaaS with B2B, B2C, and licensing paths

A pre-revenue vertical software asset for vehicle-wrap concept creation, customer review, shop approval, and controlled production handoff. The recovered product evidence includes three compiled workflow deployments for shops, consumers, and a conditional production-licensing pattern. The seller also built a separate clean acquisition preview with editable source for five buyer-safe routes covering B2B, B2C, production handoff, and buyer scope. Its 27 controlled files, lint, reproducible production build, 13 automated tests, production-server stylesheet check, browser export flow, exact embedded proof verification, package checksum verification, dependency audits, and rendered five-page technical PDF sample passed at verification. The strongest buyer is a wrap/sign operator, print-software vendor, vehicle-template company, or vertical-SaaS platform with existing industry distribution. The proposed asset sale is limited to verified brands, domains, deployments, documentation, and assets expressly scheduled in the purchase agreement. Partner-specific identity, rights, media, and the separate mixed-use backend and licensed geometry are excluded unless separately verified and scheduled. The startup has $0 verified revenue, $0 ARR, and zero paying customers. The recovered historical products are compiled deployments, not the original editable repositories or unbundled server Functions. Historical vehicle-wrap exports are design proofs, not production-ready print files. A separate clean-source production engine now adds independently approved driver and passenger SVG sources, 1:1 millimeter calibration, scale and final-size PPI gates, versioned facility/RIP/ICC/media profiles, deterministic panels and reprint IDs, overlap and bleed controls, exact embedded render proofs, and checksummed PDF/SVG/PNG/CSV/JSON/ZIP handoff records. A buyer must still complete billing, clean infrastructure, licensed geometry, conforming PDF/X and ICC/RIP processing, printer/cutter integration, and real-world physical validation.