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Gravitnomad
Abstract cinematic visualisation of an autonomous agent operating a live advertising system, teal data streams converging into a single decision node.

Case Study Zero, Part Two

Our sales agent is running the ads that brought you here

PushVende is the autonomous sales agent we build. Rather than describe it, we handed it our own budget: it opened the Google Ads campaign for this site, wrote the targeting, and manages it daily with no one approving each step. The numbers below are read live from that operation — not a screenshot, not a mock.

Live from the operation

The campaign that served you this page

Real leads come from our own contact records; spend, clicks and campaign state are read from the Google Ads account the agent operates. This block updates on its own — if a number here is unflattering, it stays unflattering.

Reading the live numbers…

What the agent actually does

The same loop we build for clients, pointed at our own funnel: observe, diagnose, hypothesise, act, verify, measure, learn, repeat.

It decides, then acts
No approval queue. Within a closed budget and the law, the agent creates campaigns, sets budgets and bids, adds negative keywords and pauses what is not working — on its own.
It cannot overspend
Every paid action reserves its worst plausible cost before executing, against an immutable ledger. The monthly cap is enforced in code, not in a policy document — and spend is reconciled daily against the platform.
It verifies its own work
A success response is not proof. After every change the agent re-reads the platform and compares the world to what it intended; an unconfirmed change is flagged, not celebrated.
It measures the outcome that pays
Not clicks. A lead only counts when it lands in our contact records as a unique real address — the same source of truth our sales team works from.
It can be stopped instantly
A kill switch sits outside the model. When it is armed, the agent may only do protective work — pause spend, roll back a change — until a human disarms it.
It shows its work
Every observation, decision, action, cost and measurement is appended to a tamper-evident audit trail. The public cockpit is that trail, not a summary of it.

Why we run it on ourselves

The most honest demo is the one that costs us money

Anyone can show an agent in a sandbox. We gave ours a real advertising account, a real budget and a real target — ten qualified leads in seven days — and published the result before knowing it. If the agent underperforms, this page shows that too. That is the standard we hold client work to, so it is the standard we hold ourselves to first.

Abstract neural network of luminous nodes and data pathways forming an intelligent structure.

What this is, and what it is not

This is one campaign on one account with a deliberately small budget, running for seven days. It is not a claim that an agent replaces a marketing team, and it is not a benchmark — it is a working system doing real work in public.

The agent operates paid search, our site and our messaging. It does not talk to prospects for us: when a lead arrives, a person replies. The judgement about whether a project is a fit stays human.

Everything you see was built with the same engineering standards we sell: budget enforcement in code, verification against the real world, an immutable audit trail, and a stop control that does not depend on the model behaving.

Want an agent that operates, not one that demos?

If you have a process where decisions repeat daily and outcomes are measurable, that is where this shape of agent pays. Tell us the process and we will tell you honestly whether it fits.