Work
Before Airon deployed agents into client operations, the same team built agent products end to end. Four of them are below, each with its stage stated plainly. Client deployments run under NDA — the demos on the homepage show those agents working on fictional data.
The builds
These were built in AiAiOHHH, our product studio and lab. Different industries, same discipline: the agent is grounded in the operation's own records, and a person signs off before anything irreversible happens.
A creator operating system. AI agents plan, draft, repurpose and schedule social content across platforms from one dashboard, and the creator reviews before anything publishes. Supabase holds accounts and data, n8n runs the agent workflows, Stripe meters usage.
Open lykbl →A reconciliation agent for Xero. It pairs bank transactions with invoices and shows a confidence level on every match. Anything below the line routes to a person — the model never clears an uncertain match on its own. Exception review is the main screen.
Menu analysis for restaurants, read straight from Square POS data. It connects what actually sells to what it costs, and puts recommendations in the operator's terms: what to change on the menu, and what the change is worth. Each recommendation carries the sales numbers behind it. The owner decides.
A listing-to-video workflow for real estate agencies, built on Reeltor. Listing details go in; branded videos, captions and portal assets come out — after the person responsible for the campaign approves them. Marketing keeps brand control while production runs in the background.
Open Property Progress →The build sequence behind these — the forge — is the same one that runs inside every client pilot. See how it runs.
The engagement
Client work starts with an inference audit: one workflow mapped, a governed pilot in four to six weeks, and a keep-or-stop number at the end.
One workflow, mapped end to end, and a straight answer: where AI belongs, and where it doesn't. The map is yours either way.
Four to six weeks. A working agent in the real workflow, measured against a baseline agreed up front. At the end, a number — and the keep-or-stop call made on it.
The proven agent moves to production — on-premise, the airon appliance, or approved cloud where you allow it, decided by what its records require. It runs alongside the AI you already use.
After go-live we keep watch on quality, cost and model behaviour as the work changes. The audit record stays current, ready for your clients to inspect. When the numbers support it, the next workflow gets mapped.
The first conversation is about the work, not the technology. If an audit is the right next step, we scope one. If it is not, we say so.