AI automation

AI that operates the marketing, not just reports on it

ADEX builds AI workflows and agents — on Claude API, n8n, Make and custom code — that monitor, report, alert and act within explicit rules, so routine marketing operations run themselves without handing over budget control.

Design my first AI workflow
Rules-firstautomation with guardrails

The problem

Marketing teams lose hours every week to work that is important but mechanical: pulling reports, reconciling numbers, watching for anomalies, moving data between tools. It is exactly the kind of work that quietly caps how much a small team can operate.

The reflex — let AI run the campaigns — is the wrong first move. Handing budget decisions to an agent with no guardrails is how you wake up to a spend spike nobody approved. The useful version automates the routine first and keeps the risky decisions gated.

How ADEX runs it

  1. 01

    Tier 1 — reporting and sync

    Automate recurring reports, notifications and data sync between platforms, MMP, warehouse and CRM. Zero budget risk, immediate time back.

  2. 02

    Tier 2 — rules and alerts

    Rule-based optimization and anomaly alerts: flag or adjust within limits the team defines, so nothing changes outside an agreed boundary.

  3. 03

    Tier 3 — assisted agents

    Multi-step agents that prepare decisions — diagnose, draft the change, quantify the impact — and wait for human approval before acting.

  4. 04

    Tier 4 — bounded autonomy

    Where it is proven safe, agents act inside pre-approved limits only, with every action logged and reversible.

  5. 05

    Guardrails first

    No uncontrolled budget changes, ever. Limits, approvals and logging are designed in before any agent touches a live account.

Proof

  • Built on Claude API, n8n, Make and custom agents — the automation fits the stack, not the other way around.
  • Automations plug into the same BigQuery layer ADEX builds for dashboards, so agents act on reconciled data, not platform noise.
  • Every tier is opt-in: you decide how much autonomy to grant and can pull it back at any time.

Safe autonomy depends on clean data and well-defined rules. We start at the tier your data and risk tolerance support, and only move up when the guardrails are proven.

What you get

Foundations

  • Automation and agent map
  • Data and tool integration
  • Guardrail and approval design

Build

  • Reporting and alert workflows
  • Rule-based optimization
  • Assisted or bounded agents

Operation

  • Action logging and audit trail
  • Documentation and handover
  • Monitoring and iteration

When this is right

  • Your team loses hours to reporting, reconciliation and manual monitoring.
  • You want leverage from AI without handing over budget control.
  • Your data is clean enough for automation to act on reliably.

When it isn't

  • Your tracking and data are unreliable — automating on bad data scales the errors.
  • You want a fully autonomous agent to run spend with no guardrails — that is not something we build.
  • The process you want to automate is not yet defined by a human — automate a known process, not chaos.

Frequently asked questions

Can AI agents optimize paid media campaigns safely?
Yes, within limits. Safe automation starts with reporting and alerting, moves to rule-based changes inside defined boundaries, and only grants bounded autonomy where it is proven safe — never uncontrolled budget changes.
What tools do you build on?
Claude API, n8n, Make and custom agents, integrated with your platforms, MMP, warehouse and CRM.
Will an agent change my budgets on its own?
Only if you explicitly grant bounded autonomy within pre-approved limits, with every action logged and reversible. By default, agents prepare decisions and wait for human approval.
What should we automate first?
The routine, zero-risk work: recurring reports, notifications and data sync. It buys back time immediately and builds trust before anything touches spend.
Do we need clean data first?
Yes. Automation acts on whatever data you feed it, so reliable tracking and a trustworthy data layer come before agents act on it.
Can we start small?
That is the recommended path — begin at Tier 1, prove the guardrails, and move up only as far as your data and risk tolerance support.

Ready to automate the routine, safely?

Design my first AI workflow

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