// THE BRIEF
Automotor.ai supplies replacement engines and transmissions for cars, trucks, and boats. High-ticket, high-intent purchases where the buyer is searching for an exact fit (year / make / model / engine code) — which makes it a precision lead-engine and quote-conversion problem, not a brand-awareness problem.
// WHAT WAS BROKEN
- ▸Buyers search by exact vehicle spec — the catalog has to map to fitment queries or the demand is invisible.
- ▸High-ticket parts convert on trust signals (warranty, certification, shipping) surfaced at the right moment.
- ▸The path from "searching for an engine" to "requesting a quote" has to be frictionless or the lead leaks.
// HOW WE SHIPPED
- 01
Week 1
Fitment + demand mapping
Mapped the catalog to year/make/model/engine fitment queries so each high-intent search has a landing surface. Identified the trust signals — warranty, certification, nationwide shipping — that move a high-ticket buyer.
- 02
Weeks 2–5
Lead engine + quote flow
Built a precision lead engine around fitment search and a streamlined quote-request flow (funnel.engine) that captures vehicle details and routes the inquiry to close, minimizing drop-off on a considered purchase.
- 03
Weeks 5–8
Trust + conversion layer
Surfaced warranty, certification, and shipping proof at the decision moment. Instrumented attribution from first search to quote so spend and content compound toward the channels that actually close.
- 04
Ongoing
Optimize quote-to-close
Continuously tune the quote flow and follow-up so more high-intent searches convert into quotes, and more quotes convert into orders.
// WHERE IT LANDED
Automotor.ai is the high-ticket commerce pattern we deploy: capture exact-fit intent, build trust at the decision moment, and engineer the quote-to-close path. Representative of our lead + conversion engine work for considered, high-AOV purchases.
// THE STACK SHIPPED