The Problem
A marketing team of 8 produces a monthly management report for 5 stakeholders. The report pulls together data from 12 different sources: Google Analytics, Google Ads, Facebook Ads, LinkedIn Ads, HubSpot CRM, Stripe, newsletter tool, SEO tool, heatmap tool, customer surveys and two internal Excel spreadsheets.
A senior analyst spends 5 hours a month just exporting the data by hand, merging it in Excel, building charts and formatting the report. The process is error-prone — in 3 of the last 12 months there were after-the-fact corrections due to transcription errors.
- 12 data sources — all exported and merged by hand
- 5h/month of senior-analyst time — for purely mechanical work
- 25% error rate — transcription errors in 3 of 12 reports
- No anomaly detection — unusual metrics are only spotted when someone looks closely
The Solution
An automated reporting system that connects all 12 data sources via API, builds in AI-driven anomaly detection and emails the finished, formatted report automatically:
- Data collector agent: Connects via API to all 12 sources, normalizes the data into a uniform format and stores it in an analytics database. Runs automatically on the first Monday of the month.
- Anomaly detection agent: Analyzes every metric for statistical outliers — more than 2 standard deviations from the 3-month average triggers an alert. Identifies both positive anomalies (unexpected success) and negative ones.
- Insight generator agent: Writes a 3-sentence summary for each metric group, highlights the 5 most important findings of the month and produces recommended actions.
- Report formatter: Generates an HTML report and PDF export in Forge's corporate design and sends it automatically to all 5 stakeholders.
Stack: Python for orchestration, Claude API for insight generation and anomaly interpretation, various marketing APIs, PostgreSQL for data aggregation, SendGrid for report delivery.
Results
| Metric | Before | After | Improvement |
|---|---|---|---|
| Reporting time/month | 5 hours | 15 minutes (validation) | 95% less |
| Transcription-error rate | 25% of reports | 0% | fully eliminated |
| Anomaly detection | 0 (manual) | 100% automatic | new channel |
| Report quality (stakeholders) | 7.4/10 | 9.1/10 | +23% higher |
| Time to report delivery | 3–5 days after month end | Monday 08:00 automatically | on time + reliable |
The Unexpected Payoff
Anomaly detection turned out to be the most valuable part of the solution. In the second month after rollout, the anomaly agent flagged an unexpected traffic spike on a landing page — a positive anomaly that would have been missed in manual analysis. The team traced the spike back to a Reddit mention and turned it into a targeted community strategy.
After three months the AI-generated report was better than the hand-made one — because it sees patterns a human simply stops noticing clearly after the tenth data point.
Learnings & Best Practices
- Check API reliability: 3 of 12 sources had no stable API (older Excel exports). These were handled temporarily with manual uploads via a Dropbox scan folder. Migration to API sources is running in parallel.
- Validation time is not optional: 15 minutes of analyst review before sending are mandatory. AI insights are good — but a fresh human eye on critical metrics is indispensable.
- Insight quality rises with prompt iteration: The first insight phrasings were too technical for management stakeholders. After three iterations of the insight prompt, the stakeholder rating rose from 6.8 to 9.1.
This case study is illustrative. The metrics shown are based on typical implementation experience and industry averages. It is not a real customer project and uses no real customer data.