Agentic Monthly Reporting Orchestrator

The Manual Reporting Problem

Every month, the same process: log into GA4, pull traffic data. Switch to Search Console, export keyword performance. Open Google Ads, download campaign metrics. Check SEMrush for competitive context. Import everything into spreadsheets. Cross-reference. Identify discrepancies. Write narratives. Build deliverables.

It worked. But it was slow, error-prone, and mind-numbing. Worse, it didn't scale. Every new client meant another cycle of manual data pulls and report writing.

We needed a system—something that could handle the collection, transformation, and narrative generation automatically. Not a dashboard (clients have those). An orchestrator that produces finished deliverables.

Building the Orchestrator

The system architecture follows a pipeline pattern: data sources → connectors → transforms → validators → output generators → artifact storage.

Data Connectors

We built connectors for the platforms that matter:

• Google Analytics 4: Traffic, engagement, conversions
• Google Search Console: Search performance, impressions, clicks
• Google Ads: Campaign metrics, cost data, conversions
• SEMrush: Competitive intelligence, keyword rankings
• Google Sheets: Client-specific data and configurations
• Gmail/Slack: Communication context and notes
• Supabase: Historical data storage and retrieval

Each connector handles authentication, rate limiting, and data normalization. The orchestrator doesn't care where data comes from—it works with a unified format.

Data Transforms

Raw API data isn't ready for reports. Different platforms use different metrics, time zones, and attribution models. Transforms standardize everything:

• Date alignment across sources
• Metric normalization (clicks vs sessions vs users)
• Attribution reconciliation
• Period-over-period calculations

The result is a unified dataset where GA4 traffic, Ads spend, and Search Console impressions can be analyzed together coherently.

Validation Layer

Before data reaches output generation, validators check for problems:

• Completeness: Are all expected date ranges present?
• Anomaly detection: Are any metrics unexpectedly zero or negative?
• Cross-source consistency: Do GA4 and Ads conversions roughly align?
• API health: Did any data source fail or return partial data?

Catching problems here prevents embarrassing deliverables. A report showing zero conversions because the API timed out is worse than no report at all.

From Metrics to Story

Data alone isn't useful. Clients don't want spreadsheets—they want understanding. The orchestrator includes narrative generation that transforms metrics into coherent stories.

The output follows a standard structure:

• Executive summary: What happened this month in 2-3 sentences
• Key metrics: The numbers that matter, with context
• Channel breakdown: Performance by source and campaign
• Insights: What the data tells us and what it means
• Recommendations: What to do next based on the data

This isn't template fill-in-the-blank. The narrative adapts to the data—emphasizing wins, explaining dips, highlighting opportunities.

Per-Client Artifact Storage

Every orchestrator run produces artifacts: raw data pulls, transformed datasets, validation logs, and final deliverables. These are stored per-client in Supabase.

This creates several benefits:

• Historical context: Last month's data is always available for comparison
• Audit trail: If a number looks wrong, we can trace it back to source
• Continuity: New team members can understand client history
• Trend analysis: Multi-month patterns emerge from stored data

The Results

The orchestrator transformed monthly reporting from a multi-day manual process into an automated pipeline that runs in hours. More importantly:

• Consistency: Every client gets the same quality deliverable, every month
• Accuracy: Validation catches errors before they reach clients
• Scalability: Adding clients is configuration, not workload
• Narratives: Reports tell stories, not just show numbers

The Bottom Line

Monthly reporting is necessary but not differentiated work. Every agency does it. The question is whether you're spending human hours on data collection and formatting—or on analysis and strategy.

The orchestrator handles the mechanical work. Humans focus on interpretation, recommendations, and client relationships. That's the division of labor that makes sense.