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.

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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

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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

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

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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.

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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.

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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

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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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