What changed?
Compare the right period, campaign scope, KPI, and breakdown without losing the account context.
For hands-on Meta advertisers
See what changed. Decide what to do next.
AdFlow is a local decision workspace for Meta campaigns. It connects performance monitoring, evidence-based investigation, recommendation review, experiment design, and controlled account changes.
AdFlow complements Meta Ads Manager. It organizes the reasoning and review work that usually gets split across dashboards, notes, and spreadsheets.
01
One campaign scope
02
Evidence-linked findings
03
Human-controlled changes
The operating problem
The metric is rarely the whole problem.
Meta can show that performance moved. The harder work is preserving the context behind the next decision.
What supports the explanation?
Keep every finding tied to named campaigns, source metrics, data quality, and a frozen snapshot.
What should happen next?
Separate an idea, a learning plan, and an account change so each receives the right review.
The AdFlow workflow
One campaign question. Five deliberate stages.
The same scope moves forward while each workspace adds a specific kind of context or authority.
Monitor
See delivery, spend, results, targets, and recent account changes in context.
Investigate
Select the campaigns and dates that matter, then freeze that scope for analysis.
Decide
Review evidence-linked findings and turn the useful ones into editable recommendations.
Test
Give the recommendation a hypothesis, fixed cells, one variable, and a stopping rule.
Control
When Meta setup must change, inspect the live value and approve the exact proposed value.
Why AdFlow exists
A decision layer—not another reporting dashboard.
AdFlow is designed around the handoffs between seeing a signal and changing an account.
Meta Ads Manager remains the source for delivery and account controls. AdFlow keeps the reasoning around those controls explicit.
- The selected campaign scope survives the handoff into analysis.
- A finding carries evidence and limitations instead of becoming a detached AI answer.
- A recommendation stays editable and does not write to Meta.
- An experiment defines what should be learned before launch.
- An Execution Plan contains the exact account mutation and its approval state.
Learning and authority
A recommendation is not a command.
AdFlow keeps two decisions separate: what you want to learn and what the Meta account is allowed to change.
The learning decision
Experiment review covers the hypothesis, control and variant, isolation variable, metric, sample, runtime, and stop rule.
The account decision
Execution review covers the live Meta value, proposed value, guardrails, approval, confirmation, outcome, and possible rollback.
Hosted demo
Explore the workflow with sample data.
The public demo shows the product flow without connecting a live account.
Sample campaigns. View-only entry. Live Meta execution disabled.
Editing requires a temporary password unlock, demo state may reset, and no live Meta or AI credentials are included.
Understand deployment and execution boundaries