Workflow Systems · R&D Demos

AI Analytics Reporting Workflow

Exploring automated analytics analysis and executive reporting workflows

Juan Iturbe •
AI Analytics Reporting Workflow

Status

Research & Demo

This project is an experimental analytics automation workflow built to explore how LLMs can be used to interpret structured data and generate repeatable executive reports.

It is not a finished SaaS product.

Overview

This demo explores a common operational problem:

Teams collect analytics data, but insights still require manual analysis and reporting.

The system implements a deterministic analytics pipeline that:

  • Extracts metrics from Google Analytics
  • Interprets performance relative to business context
  • Produces structured, executive-friendly reports
  • Publishes results automatically to Notion

The emphasis is on workflow design and orchestration, not dashboards or visualization tooling.

Simple UI to submit URL for report generation

Workflow Overview

Architecture Summary

The workflow is orchestrated using LangGraph as a multi-step state machine, where each node performs a clearly scoped task in the analytics pipeline.

High-level stages:

  1. Data preparation
  2. LLM-based analysis
  3. Insight synthesis
  4. Report compilation
  5. Publication

Workflow Overview

Each stage operates on structured inputs and produces explicit outputs, enabling predictable execution and easy extension.

Workflow

Prepare Data
  → Analyze Performance
  → Generate Charts
  → Compile Report
  → Executive Adaptation
  → Publish to Notion

The workflow is designed to run on a schedule or on demand, producing a consistent report format each time.

Main Characteristics

Data-to-Insight Orchestration

The system demonstrates how structured analytics data can be combined with LLM reasoning to produce qualitative insights, not just summaries.

Context-Aware Analysis

Analysis is performed relative to business goals and historical context, rather than raw metrics alone.

Deterministic Multi-Step Workflow

Each step in the pipeline has a single responsibility and clear input/output boundaries, avoiding monolithic “do everything” prompts.

Automated Executive Reporting

Outputs are adapted for a non-technical audience, showing how agents can transform analytical results into decision-ready artifacts.

External System Integration

The final handoff to Notion demonstrates controlled integration with external systems rather than end-to-end autonomy.

Technology Stack

  • LangGraph (JS) — workflow orchestration
  • OpenAI GPT-4 — analytics interpretation and synthesis
  • Google Analytics Data API — metrics source
  • Notion API — report delivery
  • TypeScript / Node.js — implementation
  • Supabase — configuration and metadata storage

Explore the Code

  • GitHub: https://github.com/ituPhi/alytics

  • Key areas to review:

    • Workflow graph definition
    • Node boundaries and data contracts
    • Analysis prompt structure
    • Report compilation logic
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