HT
Projects/EcomOS AI
In progress2025 — présentDesigner & developer (SaaS prototype)

EcomOS AI

E-commerce decision support: scoring, explainable recommendations, PDF reports.

EcomOS AI

Project overview

A SaaS prototype analyzing e-commerce store performance and generating actionable recommendations (push/stop products) using an explainable rule engine and automatically calculated KPIs.

Current state

  • Advanced functional demonstration prototype built.
  • Full pipeline: import, scoring, recommendations, and PDF report generation.
  • YAML rule engine operational with priority levels.
  • Currently moving toward a connected SaaS MVP with real data sources.

Tech stack

PythonPandasStreamlitReportLabYAMLJupyter

Tags & Code

SaaSDataPython

Private code (startup in development)

Vision

  • Make daily decisions simpler: what to do today, and why.
  • Move from CSV prototype to connected data (e.g., Shopify) for an MVP.

Architecture

  • Pipeline: CSV import → schema validation → cleaning → KPI calculation (ROAS, margin, CTR) → scoring → recommendations → PDF report.
  • Weighted multi-criteria scoring engine (margin, volume, ad profitability, risk).
  • YAML-based decision rule engine: nested AND/OR conditions, priority levels (SURVIVAL, GROWTH).
  • Automatic generation of contextualized recommendations and PDF reports (ReportLab).
  • Streamlit interactive UI for data exploration and decision visualization.

Roadmap

  • Stabilize scoring engine and report quality.
  • Connect to a real data source like Shopify (MVP).
  • Automation and per-store personalization (long-term).

Engineering decisions

  • YAML rule engine for understandable and configurable recommendations.
  • Fast prototype to validate business logic before industrialization.
  • ReportLab for professional PDF report generation without external dependency.

Possible improvements

  • Connect to Shopify or other real data sources.
  • Improve report quality and personalization.
  • More intelligent and automated layer (ML).

Lessons learned

  • Data quality directly impacts recommendation relevance.
  • Explainability is essential for adoption in decision-support tools.
  • A fast prototype helps validate complex business logic.

Screenshots

EcomOS AI preview

EcomOS AI preview