Use cases / Customer and revenue
AI Marketing
Content at pace, inside brand and claims rules.
01
Problem
Content demand outstrips capacity, and unreviewed AI copy invents claims, numbers and testimonials.
02
AI capability
Draft and adapt content, check it against claims and brand rules, analyze campaign results and propose experiments.
03
Data
The agent works from the context you connect, not from the open internet by default.
- Brand and claims guidelines
- Product documentation
- Campaign analytics
- Audience segments (aggregated)
04
Agent
Marketing Agent
Drafts and localizes content and checks it against approved claims.
05
Workflow
- 1. Brief
- 2. Draft
- 3. Claims and brand check
- 4. Editor review
- 5. Publish by a person
- 6. Measure
- 7. Report
06
Governance
Governance runs inside the agent at runtime: policy changes what it can actually do.
Can
- Read approved guidelines and product docs
- Draft and localize content
- Analyze campaign data
- Propose experiments
Cannot
- Publish live
- Invent statistics or testimonials
- Use personal data beyond consent
- Edit brand rules
Requires approval
- Publishing
- New claims
- Paid spend changes
Records
Agent identity, Data accessed, Model used, Output, Tools called, Policy applied, Decision, Approval, Action, Outcome.
Suggested starting autonomy: L2 Approve
Start at L2: an editor approves each asset; consider L3 for low-risk internal drafts.
07
Outcome
What you measure, so the agent earns more autonomy on evidence:
- Time from brief to approved asset
- Editor revision rate
- Claims flagged before publishing
- Campaign performance versus baseline
Questions
Can it publish to the website?
No. Publishing is a restricted action reserved for people.
How are unsupported claims prevented?
Drafts are checked against an approved claims list and unsupported statements are flagged for the editor.