[](/) Case study · Build · Scale # Revyautos. Honda-backed automotive marketplace, zero to scaled production. Two years as CTO and architect end-to-end. Built the technical team. Shipped 10,000 listings. The whole platform launched on top of the Wilde Agency codebase foundation — that's why a team of 12 engineers could ship the surface area of a marketplace plus payments plus AI inquiry handling plus multi-channel syndication in the time and budget Revyautos had. The engagement 10,000 listings shipped 20 people at peak 12 engineers Q1 24 → Q4 25 CTO end-to-end The mandate ## Take a C2C automotive marketplace from zero to revenue, fast — without the platform breaking under early traction. Honda's investment thesis assumed an AI-augmented seller experience could change the unit economics of selling a used car between consumers. The mandate was simple and unforgiving: build the marketplace, the seller tools, the AI inquiry handling, the payments, the multi-channel syndication — and do it on a runway that didn't allow for re-architecture later. The approach ## CTO and architect end-to-end. One foundation. A tight loop with PM and GTM. Built on the Wilde Agency codebase foundation — auth, billing, observability, CI/CD, deployment, background jobs already solved before the first Revyautos-specific line was written. The team didn't waste a sprint on plumbing. Every story shipped against marketplace logic, not infrastructure. Production‑grade integrations from day one: **KeySavvy** for title transfer, **CometChat** for buyer–seller messaging, **Courier** for multi‑channel notification orchestration. No homegrown reimplementations of solved problems — the team's time went into the parts that were uniquely Revyautos. A 12‑engineer team built in a tight loop with product and GTM. Weekly demos. No phased "discovery → design → build → demo" cadence. Code in front of real users within weeks of starting. AI was deployed only where it added real value: writing assistance for sellers, automated buyer‑inquiry handling, marketing copy generation. Chat‑for‑everything was tried and dropped early — users wanted structured flows. Revy AI ended up handling 26 of 26 inquiries on the dashboard view shown below, freeing sellers from manual response work. What we shipped [](/work/revyautos/marketing-home.png "Open full-size") Marketing surface — list a car in minutes, $1 to list. Friction removed. [](/work/revyautos/showroom.png "Open full-size") Showroom search — 2,673 active listings nationwide at peak. [](/work/revyautos/vehicle-detail.png "Open full-size") Buyer‑side vehicle detail with RevyPay integrated payments and inquiry CTA. [](/work/revyautos/seller-dashboard.png "Open full-size") Seller dashboard. Revy AI handled 26 of 26 inquiries — structured flows where AI adds real value. [](/work/revyautos/enrichment.png "Open full-size") Listing enrichment workflow — service history, ID verification — staged for buyer confidence. What it taught us ## Chat‑for‑everything fails. Structured flows with AI where it counts wins. The first version assumed buyers and sellers would want a chat box for every interaction. They didn't. They wanted forms. Listings. Decision support. The team fell back to structured flows for the bulk of the experience and deployed AI only at the points where the human‑in‑the‑loop cost was highest — writing assistance, buyer‑inquiry handling, marketing copy. Engagement went up. Support tickets went down. That insight is now the operating principle behind every AI‑augmented surface we build for clients. ## Want this kind of build behind your venture? The Wilde Agency foundation, deep experience that owns it end‑to‑end, and the team to ship. [Book an architecture review →](/start?utm_campaign=case-revyautos) Got a question?