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Agentic AI5 min read

A Trucking Maintenance Shop in Middle Tennessee Doesn't Need an SDR Team. It Needs a Swarm.

A shop owner great at fixing trucks should not have to be great at sales too. We built an agent swarm that prospects, qualifies, and books, so it can grow.

Justin Hinote

Partner & AI Business Consultant

Concentric glowing rings radiating from a bright point on a dark gradient background

A trucking maintenance shop in middle Tennessee called us with a problem that sounds simple on the surface.

Good technicians. Solid reputation. Plenty of trucks on I-40 that need work. But the shop wasn't growing the way it should be. Leads came in and went cold. Fleet managers never got a callback. Inbound inquiries sat in a voicemail or a contact form until it was too late. The owner knew exactly what was happening and had no idea how to fix it — because fixing trucks is what he knows, not chasing down fleet accounts.

Hiring a salesperson wasn't realistic. The margins in shop work don't support a full-time SDR, and finding someone who understands both trucking and sales is harder than it sounds. So the leads kept dying.

That is the problem we built for.

What the swarm does

We built a four-agent system custom to this shop's workflow. Here is what each one handles.

Prospecting. The first agent finds potential customers — fleet operators, owner-operators, logistics companies, and construction outfits running heavy equipment — within the shop's service radius in middle Tennessee. It pulls from commercial databases, public records, and DOT carrier data. It filters by fleet size, equipment type, and proximity. It writes outreach to each one in the shop's voice, referencing what it found: the kind of equipment they run, how far they are from the shop, what a PM relationship would actually be worth to them.

Qualification. When someone responds — or when an inbound inquiry comes in through the website or phone system — the second agent picks it up. It asks the right questions: how many trucks, what type, what their current maintenance situation looks like, when their last DOT inspection was. It builds a picture of whether this is a one-time repair call or a real fleet account worth pursuing. It does this through text or email, at whatever hour the lead comes in.

Follow-up and nurture. Most leads don't convert on first contact. The third agent knows this. It runs a sequenced follow-up — not spam, not the same message twice — that stays relevant to what the prospect said during qualification. A fleet manager who mentioned they run refrigerated units gets follow-ups about refrigeration system PMs. One who mentioned a previous shop let them down gets something that addresses reliability. The agent keeps the shop in front of the right people without anyone at the shop doing anything.

Booking. When a prospect is ready to bring a truck in, the fourth agent moves them into the appointment workflow. It confirms details, sets expectations, and makes sure the right information is in front of the technicians before the truck rolls in. No dropped handoffs, no missed scheduling.

What the owner actually does

He talks to the swarm through his phone. Not a dashboard, not a web app he has to remember to check — his phone. He tells it which areas he wants to focus on. He tells it when he has capacity. He tells it what kinds of accounts he's looking for. The swarm handles the rest and surfaces anything that needs his attention.

That is it. The shop owner does shop owner things. The swarm does the sales work.

This is not a product you buy

There is no off-the-shelf version of this. We mapped this shop's actual workflow, built agents around how the business actually operates, and connected it to the data sources that exist in the trucking world specifically. A dental practice needs different prospecting sources. A commercial cleaning company has a different qualification process. A staffing firm books differently.

The architecture is the same. The build is always different.

If you run a business where good leads are dying because there's no one to follow them up — not because the product isn't good and not because the customers aren't there — this is what we build.

Let's talk about it →

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Frequently Asked Questions

Why do most dispatch teams still run on phone calls and whiteboards?

Because the TMS market was designed for asset-based carriers with predictable lanes, and brokerages plus mixed-fleet operators have to bend those tools to fit. The whiteboard wins because it's faster than a screen full of fields nobody updates.

What does manual dispatch tracking actually cost in margin?

Three to seven percent of revenue, typically. It shows up as missed reimbursements from accessorials nobody filed, deadhead miles from late carrier matching, and accounting reconciliation that takes a week per cycle. The margin loss is real but hard to see because no single transaction tells the story.

Can AI handle dispatch decisions or just route them?

Both, but the right starting point is enrichment and routing — pull rate data, suggest the best carrier, draft the rate confirmation, and let a human dispatcher confirm. Once that's stable, you move to AI handling exception-light loads end-to-end with human review on the ones that matter.

Do we need to replace our TMS to fix this?

No. Most of the gain comes from AI agents reading from and writing to your existing TMS — McLeod, AscendTMS, Tailwind, doesn't matter. Replace the TMS only if you've outgrown the data model, not because the automation requires it.

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Want to put this into practice?

Book a 30-minute call. We'll talk through how this applies to your business and where the biggest opportunities are.

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