AI automation that does not add to the approval backlog
Agents are quick to set up. The difference lies in how much human supervision they permanently consume — that is where we start.
AI automation means letting models and agents execute recurring marketing and operations tasks under clearly defined approval levels. The decisive factor is the autonomy level per task: what runs fully automatically, what is sampled, and what always requires sign-off.
Our approach: creative engineering
We work neither as tool consultancy nor as a classic production agency, but in between: creative engineering means building the production chain behind the creative work — templates, generative modules, checkable brand rules and delivery as one system. The creative judgement stays with the team, the multiplication becomes an engineering task.
The concept stays human
Idea, visual language and tone are created as before. What follows is what gets automated: variants, formats, language versions, delivery.
Brand rules the machine can check
Colour, typography, logo usage, forbidden claims — stored as rules that apply automatically before approval instead of sitting in a PDF guideline.
Production as a chain, not a tool
Template, generative module, validation step, approval and delivery interlock. A single tool without that chain only moves the manual work elsewhere.
Impact measurable at creative level
We evaluate not only the campaign but the individual attribute: hook, composition, colour world, length. That feeds the next production round.
What we take on
Workflow automation
From briefing to reporting: recurring processes as robust pipelines instead of a prompt collection.
Marketing agents
Agents with a defined scope of action, tool access and escalation path — including logging.
QA & brand guardian
Automated quality and brand checks, so autonomy does not come at the brand’s expense.
Integration into your stack
Connection to CMS, DAM, CRM and analytics — with permissions, roles and traceable runs.
How the engagement runs
- 01
Assessment
We review your processes, data situation and existing stack, and document where AI realistically creates impact today — and where it does not.
- 02
Prioritisation & business case
Use cases are ranked by effort, impact and risk. Every candidate gets a transparent calculation instead of a gut estimate.
- 03
Pilot in production
We build the first use case all the way into real operations — including system integration, approval flow and measurement points.
- 04
Scaling & enablement
What works gets rolled out and handed over: playbooks, training and governance, so you do not stay dependent on us.
Frequently asked questions
30 minutes, a concrete assessment
Describe your use case briefly — we will say plainly whether and how it pays off.