Your organisation
Bring your client, brand and market structure. Review access and ownership before connecting accounts.
Maaten Media OS
Better media decisions
start before launch.
Standardise your campaigns before they go live. Build a shared dataset across channels. Give specialist AI agents the context to analyse, predict and optimise your media investment.
Your platforms disagree.
Your team has to join the dots.
Different naming conventions, audience structures and reporting definitions make cross-channel decisions harder than they should be.
Media OS starts before the data is generated. A common information architecture connects campaigns, creative, audiences and results from the outset.
More than connected APIs.
Pulling five reports into one dashboard does not make their information comparable. The shared structure is what gives the data useful context.
Five layers.
One connected operation.
Govern the organisation, structure the work and build the context agents need.
- Govern
Set the organisational boundaries.
Agency, client, brand, country, business unit and user permissions define who can access and operate each part of the account.
- Standardise
Make information fit before launch.
Agree taxonomy, naming, UTMs, campaign structure, creative identifiers and audience structure before campaigns go live.
- Collect
Build your unified data lake.
Bring supported advertising sources through extract, load and transform pipelines into the Maaten unified data lake. Review Google, Meta, TikTok, LinkedIn and DV360 against your required fields, permissions and refresh needs.
- Understand
Turn records into comparable signals.
Connect campaign, audience, creative, landing page, spend and results using the shared model. Keep source definitions and limitations visible.
- Act
Give specialist agents useful context.
Scope agents for pacing, monitoring, optimisation, fatigue, leaderboards, audiences, landing pages and reporting. Evaluate predictions and recommendations, then execute supported changes within agreed permissions.
Each campaign adds context for the next.
A maintained proprietary dataset connects what ran, what happened and what changed. That history can inform subsequent analysis instead of disappearing into another reporting spreadsheet.
More data is not automatically better prediction. Coverage, quality, access and evaluation determine how useful the accumulated context becomes.
Human accountability stays visible.
Agree who can approve changes, which actions can run within limits and what needs escalation. Review recommendations and outcomes against the original objective.
See the architecture in your own context.
The five layers describe the product approach. Demonstrate current capability and agree any custom work before commitment.
Your channel mix
Confirm supported sources and actions separately. Data access does not establish campaign creation or execution support.
Your first workflow
Choose the first decision to improve and the evidence needed to measure it.
Your questions, answered.
Does this solve attribution?
A shared foundation improves comparability. Attribution still depends on your measurement definitions, available identifiers, consent, source quality and evaluation. It is not a promise of one objectively correct number.
Will agents change campaigns automatically?
Only for supported actions within the permissions and approval rules agreed for your setup. Predictions and recommendations need evaluation, and exceptions need an operating owner.
Which parts can we see working today?
We will demonstrate the relevant capability and identify what is released, what needs configuration and what would be custom engineering. The architecture is not a claim that every platform action or agent is available in every account.
Build on information you can use.
Tell us your channel mix and the work you want to improve.