PISCO is a production-grade intelligent dynamic pricing system for multi-restaurant chains. It blends machine learning (XGBoost 33-feature model with transfer learning), business rules, and competitive intelligence to optimize menu pricing dynamically - with an environmental/weather layer, event-surge detection, and strict floor/ceiling safety guardrails.
PISCO is a production-grade intelligent dynamic pricing system for multi-restaurant chains. It blends machine learning (XGBoost 33-feature model with transfer learning), business rules, and competitive intelligence to optimize menu pricing dynamically - with an environmental/weather layer, event-surge detection, and strict floor/ceiling safety guardrails.
Built the PISCO dynamic pricing engine as a monorepo: a React/TypeScript admin dashboard and a Python FastAPI backend. Engineered an improved hybrid engine blending XGBoost ML predictions with a dynamic business-rules engine, an environment/event-driven pricing layer, competitive benchmarking, and strict constraint enforcement.
Delivering a production-grade SaaS Platform system required coordinated engineering, integrations, and reliable release workflows.
Implemented an improved_hybrid_engine that picks models by per-item data volume (rules → hybrid → ML), a 33-feature XGBoost model with transfer learning for zero-shot items, weather/event pricing adjustments, and competitive strategies gated by confidence scores (>70%) and minimum baselines - with APScheduler async jobs and Supabase/PostgreSQL persistence.
Implemented an improved_hybrid_engine that picks models by per-item data volume (rules → hybrid → ML), a 33-feature XGBoost model with transfer learning for zero-shot items, weather/event pricing adjustments, and competitive strategies gated by confidence scores (>70%) and minimum baselines - with APScheduler async jobs and Supabase/PostgreSQL persistence.
Primary interface for SaaS Platform.
APIs and business workflows across repositories.
XGBoost ML : Utilizes a 33-feature model and transfer learning for zero-shot item predictions.
Built with Python, JavaScript, TSX, PLpgSQL, Shell, CSS, Dockerfile, HTML; coordinated private GitLab delivery with iterative releases across related repositor…
Delivered through structured phases - discovery, design, build, integration, and launch - with iterative releases and ongoing enhancements across a ai & saas delivery.
Manual, disconnected workflows and limited visibility across operations.
An integrated, automated ai & saas solution with a unified experience, stronger controls and measurable efficiency.
Coordinated delivery across 2 related repositories.
Active engineering footprint on private GitLab.
SaaS Platform product delivery.