Notion and Postgres integration

Description This workflow continuously validates data quality using rules stored in Notion, runs anomaly checks against your SQL database, generates AI-powered diagnostics, and alerts your team only when real issues occur. Notion holds all data quality rules (source, field, condition, severity). n8n reads them on schedule, converts them into live SQL queries, and aggregates anomalies into a global run summary. The workflow then scores data health, creates a Notion run record, optionally opens a Jira issue, and sends a Slack/email alert including AI-generated root cause & recommended fixes. Target users Perfect for: DataOps Analytics Product Data BI Compliance ETL/ELT pipelines Platform reliability teams. Workflow steps How it works 1) Notion → Rules Database Each entry defines a check (table, field, condition, severity). 2) n8n → Dynamic Query Execution Rules are converted into SQL and checked automatically. 3) Summary Engine Aggregates anomalies, computes data quality score. 4) AI Diagnostic Layer Root cause analysis + recommended fix plan. 5) Incident Handling Notion Run Page + optional Slack/Email/Jira escalation. Silent exit when no anomaly = zero noise. Setup Instructions Create two Notion databases: Data Quality Rules → source / field / rule / severity / owner Data Quality Runs → run_id / timestamp / score / anomalies / trend / AI summary/recommendation Connect SQL database (Postgres / Supabase / Redshift etc.) Add OpenAI credentials for AI analysis Connect Slack + Gmail + Jira for incident alerts Set your execution schedule (daily/weekly) Expected outcomes Fully automated, rule-based data quality monitoring with minimal maintenance and zero manual checking. When everything is healthy, runs remain silent. When data breaks, the team is notified instantly: with context, root cause insight, and a structured remediation output. Tutorial video Watch the Youtube Tutorial video About me : I’m Yassin a Project & Product Manager Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

Description This workflow transforms raw SaaS metrics into a fully automated Product Health Monitoring & Incident Management system. It checks key revenue and usage metrics every day (such as churn MRR and feature adoption), detects anomalies using a statistical baseline, and automatically creates structured incidents when something unusual happens. When an anomaly is found, the workflow logs it into a central incident database, alerts the product team on Slack and by email, enriches the incident with context and AI-generated root-cause analysis, and produces a daily health report for leadership. It helps teams move from passive dashboard monitoring to a proactive, automated system that surfaces real issues with clear explanations and recommended next steps. Context Most SaaS teams struggle with consistent product health monitoring: Metrics live in dashboards that people rarely check proactively Spikes in churn or drops in usage are noticed days later There is no unified system to track, investigate, and report on incidents Post-mortems rely on memory rather than structured data Leadership often receives anecdotal updates instead of reliable daily reporting This workflow solves that by: Tracking core health metrics daily (revenue and usage) Detecting anomalies based on recent baselines, not arbitrary thresholds Logging all incidents in a consistent format Notifying teams only when action is needed Generating automated root-cause insights using AI + underlying database context Producing a daily “Product Health Report” for decision-makers The result: Faster detection, clearer understanding, and better communication across product, growth, and leadership teams. Target Users This template is ideal for: Product Managers & Product Owners SaaS founders and early-stage teams Growth, Analytics, and Revenue Ops teams PMO / Operations teams managing product performance Any organization wanting a lightweight incident monitoring system without building internal tooling Technical Requirements You will need: A Postgres / Supabase database containing your product metrics Slack credentials for alerts Gmail credentials for email notifications (Optional) Notion credentials for incident documentation and daily reports An OpenAI / Anthropic API key for AI-based root cause analysis Workflow Steps The workflow is structured into four main sections: 1) Daily Revenue Health Runs once per day, retrieves recent revenue metrics, identifies unusual spikes in churn MRR, and creates incidents when needed. If an anomaly is detected, a Slack alert and email notification are sent immediately. 2) Daily Usage Health Monitors feature usage metrics to detect sudden drops in adoption or engagement. Incidents are logged with severity, context, and alerts to the product team. 3) Root Cause & Summary For every open incident, the workflow: Collects additional context from the database (e.g., churn by country or plan) Uses AI to generate a clear root cause hypothesis and suggested next steps Sends a summarized report to Slack and email Updates the incident status accordingly 4) Daily Product Health Report Every morning, the workflow compiles all incidents from the previous day into: A daily summary email for leadership A Notion page for documentation and historical tracking This ensures stakeholders have clear visibility into product performance trends. Key Features Automated anomaly detection across revenue and usage metrics Centralized incident logging with metadata and raw context Severity scoring based on deviation from historical baselines Slack and email alerts for fast response AI-generated root cause analysis with recommended actions Daily product health reporting for leadership and PM teams Optional Notion integration for incident documentation System logging for observability and auditability Fully modular: you can add more metrics, alert channels, or analysis steps easily Expected Output When running, the workflow will generate: Structured incident records in your database Slack alerts for revenue or usage anomalies Email notifications with severity, baseline vs actual, and context AI-generated root cause summaries A daily health report summarizing all incidents (Optional) Notion pages for both incidents and daily reports System logs recording successful executions Tutorial video: Watch the Youtube Tutorial video About me I’m Yassin a Project & Product Manager Scaling tech products with data-driven project management. 📬 Feel free to connect with me on Linkedin

Filter incoming emails via IMAP, extract & clean the body into plain text, normalize (title/slug/snippet/date/from/url), check for duplicates in Postgres, store as a row in a Notion database, then send a Telegram notification. Perfect for building a knowledge base or inbox triage automation without duplicate noise. Key Features IMAP polling (Only UNSEEN). Extract & clean HTML → plain text. Normalization: title, snippet, bodyText, slug, messageId, sentAt, fromAddress, sourceUrl. Deduplication via Postgres (SELECT EXISTS). Create page in Notion database (property mapping). Telegram success notification. Tags: email, imap, notion, postgres, deduplication, telegram, automation, knowledge-base Prerequisites IMAP access (Gmail/Outlook/Custom). Gmail: enable IMAP + App Password if using 2FA. Notion Integration Token & Database ID (database must be shared with the integration). PostgreSQL accessible from n8n. (Optional) Telegram Bot Token & Chat ID.