A dealership's AI platform drafted content from its own product knowledge
Vendor documents and recorded interviews supplied material in the buyers' trade language.
Machines draft posts from a dealer's own spec sheets in the trade's words, and I read every post before it's scheduled.
Context
A two-principal dealership sells positioning technology for construction equipment into a remote-logistics market: the systems that let an excavator cut grade off a 3D model instead of wooden stakes. Their deepest asset is the senior partner, whose product knowledge his co-owner rates as about the deepest in the country. Decades of specifics: what fits which fleet, what actually breaks in the field, which twenty-second remote fix gets a stranded machine moving on a job that loses money every hour it stands still. All of it lived in one head.
What a dealership knows sits on a wall behind one man, in manuals, torn spec sheets and pinned notes, and in the part he never wrote down.
Problem
The knowledge worked only when a specific human was in the room. The market is finite and buyers learn for years before they buy, so growth means publishing what the principals know, continuously, in a voice their customers trust. The owners had no spare forty hours a week, and their previous agency shipped boilerplate. The owner's rule for content: if the customer can tell the writer does not understand their plight, the post has negative value.
A machine stops on a remote job site, somebody calls the shop, and a twenty-second remote fix gets it moving again. That worked exactly as long as one specific person picked up.
Approach
I built an agent platform that reads the vendor's brochures, spec sheets, recorded interviews and support stories, and drafts from them for every channel.
Each note the dealer sent back became a rule the next drafts follow.
Stack
- Corpus ingestion across vendor docs, recorded interviews, and support stories
- Retrieval and generation grounded in the dealership's own knowledge
- Voice controls tuned to recordings of the owner, because his customers trust his voice
Result
The platform is live and owned by the client. The knowledge that used to require the senior partner in the room now shows up wherever his market reads, while he keeps the judgment calls and the 6 a.m. rescue calls no machine touches.
Impact
Every job's results feed back in, so the dealership's understanding of its own market sharpens with use, and the owner reads all of it in his own trade language.
Lessons
Build the system in the client's own trade words. The dealer runs this one in yard terms, and each note he sends back becomes a rule the next drafts follow.
Who this is for
For owners who need to explain an AI system to their own people in their own trade's words.
Get a quoteThe method behind this result
- Content StrategyContent strategy can start with the answers editors already have. A concept map shows which pages need new links and which reader questions still need an article.
- Standard Model of Effective MediaThe Standard Model of Effective Media connects familiar details to difficult subjects. Creators test the connection against what audiences understand and do.