A multi-source pipeline that pulls live data from payments, CRM, project management, and web analytics, consolidates it into one KPI set, has AI write an executive-level analysis, runs threshold alerting, and delivers a daily summary plus a weekly HTML report — automatically.

Business-critical data is scattered across payments, sales, engineering, and marketing tools. Building a cross-platform executive report by hand takes hours a week and is usually stale by the time it's read. Leadership finds out about problems — revenue drops, ticket backlogs — late, because nothing is actively watching for them.

On a schedule, and on demand via webhook or manual trigger, the system pulls live data from Stripe, HubSpot, Jira, and Google Analytics in parallel, merges and normalizes it into one consistent dataset, and calculates the KPIs leadership actually cares about. An AI step turns those numbers into a written executive analysis rather than a wall of figures. An alert engine checks the results against configured thresholds — if something crosses a high-priority line, it notifies executives immediately over Slack and email instead of waiting for the next scheduled report. Every run is stored for history and written to an audit log, with a full HTML report going out weekly alongside the daily summary.

Three trigger modes — schedule, webhook, manual — the same pipeline runs on a cadence, on demand, or in response to another system

Parallel multi-source fetching, merged and normalized into one schema — real ETL, not a single API call

AI narrative layer on top of raw KPIs — leadership reads an analysis, not a spreadsheet

Threshold-based alerting separate from routine reporting — urgent issues don't wait for the next scheduled email

Historical storage and audit logging — every run is queryable later

Two report cadences (daily summary, weekly full report) built from the same underlying pipeline