Marketing Automation
That Acts, Not Just Answers.
AI systems built to research, analyse, decide and execute multi-step marketing workflows — automation that operates the account, not a chatbot that describes it. See the live interactive demonstration on the homepage.
A chatbot describes. An agent acts.
Most "AI in marketing" is a chat window that summarises a report you could have read yourself. An agentic workflow is different: it runs a defined loop — read the data, observe, investigate, reason about what's actually wrong, recommend a specific costed action, execute it once authorised, then verify the result and start again. That loop is what the Performance Marketing Agent demonstration on the homepage walks through, stage by stage, on sample data.
The distinction matters because it changes what the system is for. A summary is something you read once. A workflow is something that runs the same way every time an account needs it — which is the actual promise of automation, not novelty.
What I build
- Account analysis workflows — the seven-stage loop (Ads Data, Observe, Investigate, Reason, Recommend, Act, Verify) demonstrated live on the homepage
- Lead intelligence — capture, enrichment, intent scoring and routing before a human opens the record
- Marketing research — automated competitor and positioning research, synthesised into a report rather than raw data
- Reporting — consolidating paid channels and writing the explanation behind the chart, not just the chart itself
- Workflow automation — n8n, Make and Zapier connecting the tools an account already runs on
- AI-assisted product development — the same approach used to design and ship Budgetics, a published app
See it, don't just read about it
The strongest proof for this service is the system itself. Both are already live on this site.
How I build these systems
What does the workflow read, what does it decide, and where does it stop and wait for a human — designed before any automation platform gets touched.
An agent is only as good as the tracking and reporting feeding it — see Tracking & Measurement.
Recommendations are prepared; a human approves before anything executes. This is a design constraint, not a limitation.
The system re-measures, stores what it learned, and runs again — that repetition is what separates an autonomous system from a one-time answer.
Questions I get asked
What's the difference between this and just using ChatGPT on account data?
A chatbot answers a question you ask it. An agentic workflow runs a defined loop on its own — reading data, investigating, reasoning, recommending and, once authorised, acting — the same way the interactive Performance Marketing Agent demonstration on the homepage works. The homepage demo runs on sample data and does not connect to or modify a live account; it exists to show the workflow, not to claim a live integration. The full breakdown of that distinction is in what makes an AI agent different from a chatbot in marketing.
Do actions execute automatically, without a human involved?
No. Every workflow is built with authorisation as a required step before anything executes — a recommendation is prepared, a human approves it, then it runs. AI does not independently make or claim credit for business results in this setup.
What tools does this run on?
Depends on the account: n8n, Make and Zapier for workflow automation, and OpenAI's and Claude's APIs for the analysis and reasoning layer. The same AI-assisted approach was used to design and ship Budgetics, a published budgeting app — this isn't automation in the abstract, it's the same method applied to a shipped product.
Related services
Curious what an agentic workflow would look like on your account?
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