Automated Insight Generation and Analytical Storytelling System
Transform your data into executive-ready reports that explain what happened, why it happened, and what to do next.
The AI Report Factory is designed to automate data storytelling and reporting workflows for modern analytics teams, startups, and enterprises.
Its purpose is to bridge the gap between data presentation and business understanding, turning rows of numbers into clear, narrative-driven insights.
- Startups: Automated monthly investor or product reports.
- Data Teams: KPI and trend visualization without manual Excel or BI setup.
- Enterprises: Recurring operational performance summaries with executive narratives.
- Consultants: Automated client reporting templates ready for Slack or email distribution.
By merging data analytics, visualization, and storytelling, AI Report Factory helps decision-makers see, interpret, and act on their data with minimal effort.
AI Report Factory processes structured data, analyzes it for business insights, and produces professional Markdown and HTML reports that include:
- Executive KPI summaries
- Visual trend analysis
- Category, channel, and geography breakdowns
- Clear narrative explanations accompanying every visualization
It’s built with modular Python design, so each step (data prep, analysis, visualization, and report rendering) can be easily extended or replaced.
The system follows a consistent framework for insight generation:
| Step | Description |
|---|---|
| Data Ingestion | Loads structured data (CSV, SQL, or API) into a clean Pandas DataFrame. |
| KPI Calculation | Computes core business metrics such as revenue, profit, orders, AOV, and margin. |
| Segmentation | Groups results by category, sales channel, and geography. |
| Visualization | Creates charts illustrating temporal, categorical, and spatial insights. |
| Narrative Generation | Adds plain-language analysis and next-step recommendations. |
This structure enables clear, data-driven storytelling suitable for both technical and non-technical audiences.
| KPI | Description | Business Interpretation |
|---|---|---|
| Revenue | Total sales generated | Measures business scale and market reach. |
| Profit | Earnings after cost deduction | Indicates efficiency and sustainability. |
| Orders | Number of unique transactions | Reflects demand volume. |
| Customers | Unique customer count | Captures reach and retention. |
| Average Order Value (AOV) | Average revenue per order | Reveals customer purchase behavior. |
| Profit Margin | Ratio of profit to revenue | Indicates operational performance and cost control. |
These KPIs provide a top-down understanding of company health, guiding where to focus operational or marketing efforts.
Tracks monthly revenue and profit, showing both scale and efficiency trends.
- Observation: Noticeable growth in Q2 and Q4, typical of seasonal promotions or product launches.
- Interpretation: A mid-year dip suggests discounting or elevated costs; late-year recovery signals improved pricing discipline.
- Recommendation: Review pricing strategy and promotional timing to sustain profitability.
Breaks down revenue by product category to identify top performers.
- Observation: Electronics dominates, followed by Home and Sports.
- Interpretation: High-value, technology-driven categories fuel top-line performance.
- Recommendation: Cross-sell complementary goods in high-margin categories like Beauty to increase lifetime value.
Illustrates revenue share across Web, Mobile App, Retail, and Marketplace.
- Observation: Web holds the largest share (~45%), but mobile channels show rapid growth.
- Interpretation: The digital-first model is working, but multi-channel diversification can mitigate risk.
- Recommendation: Strengthen mobile and marketplace integration for resilience and customer reach.
Ranks top-performing cities by total sales volume.
- Observation: Major global markets (Berlin, London, New York) lead, followed by emerging cities like Mumbai.
- Interpretation: Core urban centers remain revenue anchors, but emerging regions show expansion opportunities.
- Recommendation: Optimize ad spend by balancing established regions with fast-growth markets.
- Data Loading → Reads CSV or API data and performs basic validation.
- Processing → Computes KPIs, time-based aggregates, and category/channel splits.
- Visualization → Generates static charts (
.png) inoutput/assets/. - Report Rendering → Populates Markdown (
report.md) and HTML (report.html) templates. - Distribution → Ready for Slack, email, or other publishing integrations.
This workflow automates reporting cycles from hours of manual work into a single command-line execution.
ai_report_factory/
│
├── __init__.py # Initializes package
│
├── reporting.py # Loads and preprocesses raw data
│ # Adds derived metrics like profit, order_month
│
├── analysis.py # Core KPI computations and aggregations
│ # Includes time-series, category, channel, and geo summaries
│
├── charts.py # Creates static visualizations (Matplotlib)
│ # Functions: save_timeseries_chart, save_category_chart, etc.
│
├── renderer.py # Jinja2-based Markdown and HTML report generator
│
├── pipeline.py # Main orchestration layer, calls reporting, analysis, charts, renderer
│
├── templates/ # Jinja2 templates for rendering
│ ├── report.md.j2 # Markdown layout for report.md
│ └── report.html.j2 # HTML layout for report.html
│
├── publisher/ # Ready for extensions: Slack, Email, Medium integrations
│ ├── emailer.py # SMTP email sender stub
│ ├── slacker.py # Slack webhook stub
│ └── medium.py # Medium publishing stub (placeholder)
│
├── data/ # Input data folder
│ └── orders.csv # Sample synthetic e-commerce dataset
│
├── output/ # Generated results folder
│ ├── report.md # Text-based report output
│ ├── report.html # Styled HTML report output
│ └── assets/ # Visualization exports
│ ├── timeseries.png
│ ├── category.png
│ ├── channel.png
│ └── geo.png
│
├── run_demo.py # Demo runner script to execute full pipeline
└── requirements.txt # Python dependencies
AI Report Factory is built on three principles:
- Automation First: Every report can be reproduced via one command.
- Narrative Clarity: Every chart is paired with meaningful interpretation.
- Modularity: Each module can evolve independently, from static to interactive, or from descriptive to predictive analytics.
This modular design allows easy scaling from a simple Python script into a full analytics microservice.
| Area | Planned Enhancement | Impact |
|---|---|---|
| AI Narratives | GPT-driven contextual storytelling | Automatically explain trends and anomalies |
| Forecasting Models | Prophet or ARIMA time-series predictions | Predict future KPIs |
| Interactive Visuals | Plotly or Streamlit | Create dynamic exploration dashboards |
| Automation | Airflow or GitHub Actions integration | Schedule periodic reporting |
| Multi-Source Data | Integrate with APIs (Shopify, Stripe, GA4) | Build unified analytics pipeline |
AI Report Factory demonstrates how analytics can evolve from static reporting to dynamic storytelling.
By merging data processing, visualization, and interpretation, it empowers teams to understand their data faster and communicate insights more effectively.
Every chart and metric becomes a part of a larger story, one that guides strategic action.