AI Automation by Industry: What We've Actually Built

AI automation by industry looks like the same six or seven jobs wearing different clothes: a daily report instead of ten phone calls on a plantation, a disclosure line enforced in a creator brief, a prior authorization packet assembled instead of retyped in a clinic. We install a tomte, our word for one production agent with one defined job, for each of those jobs and leave the judgment calls to a person. Below is what we’ve actually built, industry by industry, with the full build linked under each one.

What ties these builds together?

Every build below follows the same rule: the agent does the repetitive middle of the job, and a person keeps the parts that carry risk or need a relationship. None of them is a single platform that “does AI” for an industry. Each is five or six small, named jobs, with an owner and a review queue, installed in a business we watched do the work by hand first.

We did not start from an industry list and go looking for jobs to automate inside it. We started from one job at a time, mostly inside our own three-company group, and only noticed afterward that the same shape repeats: something gets retyped, something gets re-explained on a call, something goes quiet and nobody notices for a week. The industry below the section heading is where we happened to build it. The mechanics travel further than the label suggests.

Plantation and estate operations: one report instead of ten calls

A plantation tomte reads structured field logs from every supervisor’s phone once a day, writes one plain-language summary instead of a round of evening calls, and treats a day with no logged data as a problem rather than a quiet day. It does not steer equipment or predict yield. Harvest dates get projected from the actual interval between past harvests, and uploaded plant photos come back with a written health assessment. The full build covers the escalation path and what it costs to install.

Small business back office: the seven jobs that pay back first

Across the small businesses we audit, the same seven back-office jobs come up first: accounts-payable and invoice processing, customer-inquiry intake, document and data entry, recurring reports, onboarding paperwork, order confirmations, and month-end reconciliation. They rank in that order because they are high-volume, rules-based, and checkable against a source document. The full ranking, with the effort and payback score behind each one, is where we’d start on your own back office too.

Market research: one page instead of ten tabs

A market monitoring tomte reads a fixed list of named sources on a schedule, discards anything it cannot trace to a dated link, and hands a person a ranked shortlist instead of an open-ended chat answer. It never decides what gets developed further; that stays a human call, on purpose. The full build covers the scoring axes and where the automation still breaks.

Influencer campaigns: automate the record, not the relationship

A creator-campaign tomte holds the seven-stage pipeline a spreadsheet cannot: prospected through paid, with dated stage changes and a brief template that carries the required disclosure wording. It does not message creators, judge content, or negotiate terms. What it fixes is losing track of the thirty people you already contacted. The full build has the stage-by-stage split of what’s automated and what stays manual.

Clinics: admin first, medicine never

A clinic tomte sits in the front desk and billing lanes: patient inquiries, appointment reminders, prior authorization packets, claim rejections, the weekly report rebuilt from three screens by hand. It never touches a clinical decision or a patient record under a regime we have not been audited against. The full build explains why that boundary is a business rule, not modesty.

Logistics: start at the quote desk

A forwarder’s first tomte turns an inbound quote request into a structured shipment record, fans a rate request out to the carrier panel, and normalises the replies into one comparable table instead of a scroll of emails. Status questions get answered from that same record before the customer asks. Anything it cannot resolve goes to a review queue with a stated reason, never straight to the customer. The full build walks the sequence in order.

The layer that decides what reaches you

None of the builds above run unwatched forever. Our own chief-of-staff agent reads the finance reports, targets, tasks and fleet health behind every industry build here and decides which of it needs a person: a spend over a set threshold, anything irreversible, anything crossing companies. Everything under that bar it logs or handles inside a small, reversible free tier. It is the one piece on this page that is not industry-specific, because the same materiality bar applies whether the queue behind it is field reports or claim rejections.

How the builds compare

Industry	The job we automate	What the tomte hands back	Full build
Plantations and estates	Field logs, photo checks, harvest projection	One daily written report	ai-for-plantation-management
Small business back office	Invoices, intake, entry, reports, reconciliation	A ranked build list by payback	automation-opportunities-small-business
Market research	Scheduled source reads, ranking	One ranked shortlist a week	ai-market-research-automation
Influencer campaigns	Pipeline state, disclosure wording	A dated stage board, no leaks	influencer-marketing-automation
Clinics	Intake, reminders, prior auth, claims	Assembled packets, a review queue	ai-automation-for-clinics
Logistics and freight	Quote requests, status, documents	One comparable rate table	ai-automation-for-logistics-companies

Which one should you build first?

Start with whichever job is high-volume, rules-based, and checkable against a source document; that test holds in every industry above. It also explains why none of the six builds above started with the flashiest possible use of AI. A plantation agent that predicted yield would be more interesting to demo and less useful than one that simply tells a manager what happened yesterday. The interesting version usually comes later, once the boring version has run long enough to be trusted.

If you are not sure which of your own jobs clears that bar, that ranking is the entire output of an AI Operations Audit: ten working days, $1,900 fixed, a written map of your business with a ranked build list and a fixed quote for anything on it. Full refund if nothing on the list is worth building.

We run this same discipline on our own companies before we sell it. As of July 2026 our fleet stood at 41 registered agents, 9 live in production, with 5,450 runs and zero failures across one measured 13-day stretch, on the agent fleet proof page. Every one of those agents has a named owner and a fence it cannot step outside, which is the same discipline described industry by industry above.

Tell us what industry you’re in and what eats your team’s time today. Start async and you’ll get a written reply within one business day. No meetings, ever.