A portfolio-ready Make automation scenario that reads CRM and automation log data from Airtable, aggregates operational activity, calculates event-type summaries, and sends a concise Telegram report for quick business visibility.
A reporting layer for a beauty-service CRM pipeline. The scenario turns raw CRM and automation-log records into a concise operational summary that a business owner can read without opening Airtable or Make.
Operational data existed in Airtable, but the owner would still need to manually inspect lead rows and automation logs to understand daily activity.
Make reads the relevant Airtable tables, aggregates records, calculates event counts, and sends a compact Telegram report.
A repeatable daily report that proves data retrieval, aggregation, event categorization and Telegram delivery in one scenario.
The scenario uses a linear production-safe design: read source records, aggregate bundles, calculate event categories, build a report variable, then send it to Telegram. Click any module to inspect it.
A daily Make scenario: read Airtable CRM + logs, aggregate event counts, build a report, and send it to Telegram.
The scenario was manually executed after configuration. Daily 09:00 scheduling was configured but left inactive to avoid unnecessary Make operations.
| Test | Expected result | Status |
|---|---|---|
| Airtable Leads_clean read | Lead records retrieved | ✓ Passed |
| Airtable Automation_Logs read | Log records retrieved | ✓ Passed |
| Lead aggregation | 10 lead records counted | ✓ Passed |
| Log aggregation | 10 log records counted | ✓ Passed |
| Event type summary | 6 new leads, 4 duplicates, 4 missing-email events, 1 workflow error | ✓ Passed |
| Telegram delivery | Daily report delivered to bot chat | ✓ Passed |
| Schedule configuration | Daily at 09:00 configured, scenario left inactive intentionally | ✓ Passed |
Final proof screenshots from the working Make scenario, Telegram output and Airtable source tables.
Complete production-ready reporting scenario with Airtable searches, array aggregators, event-type summaries, report composition and Telegram delivery.
Final operational report with dynamic counts: 10 lead records, 10 log records, 6 new leads, 4 duplicate attempts, 4 missing-email events and 1 workflow error.
Operational event history used for lead_created, duplicate_lead, missing_email and workflow_error summaries.
Validated lead records used as the CRM source table for the daily reporting scenario.
Daily 09:00 scheduling is configured but inactive by choice. This avoids unnecessary Make operations while keeping the setup demonstrable.
Visible records are sample data created for the portfolio case study. No real client data is exposed.
Separate Airtable searches were chosen over complex inline array expressions to improve readability, reliability and debugging.
This project demonstrates Make automation design beyond basic app-to-app integration.
Built a readable scenario with clear module naming, controlled data flow and separated reporting responsibilities.
Used Airtable searches with formula filters to retrieve targeted CRM and operational log data.
Used array aggregators and __IMTAGGLENGTH__ to convert bundles into usable report metrics.
Converted raw records and event logs into a concise report for business decision-making.
Configured Telegram Bot integration for direct operational notifications.
Configured scheduling but deactivated the scenario after proof to control unnecessary operations.