Large-scale beauty & health e-commerce
Khanoumi
Multi-agent RL pricing, AI content production, Text-to-SQL BI, and cloud MLOps for a 24,000+ SKU catalog.
Overview
Context
Khanoumi required production AI systems that could operate across catalog pricing, content generation, analytics, and deployment operations without slowing business teams down.
Challenge
Problem Space
Manual pricing review, slow PDP content workflows, and ad-hoc SQL queues were limiting scale across a large product catalog and non-technical stakeholder base.
Solution Architecture
What shipped
Architected a multi-agent reinforcement learning pricing engine using historical sales, inventory signals, demand elasticity, and low-latency gRPC serving.
Built an AI content system combining RAG-based attribute extraction, LLM prompt engineering, and image-synthesis orchestration for product visuals.
Delivered a natural-language-to-SQL BI platform with LangChain and custom prompts for direct stakeholder access to revenue, margin, churn, and conversion KPIs.
Owned Airflow, MLflow, GitLab CI/CD, Docker, Kubernetes, Terraform, and AWS Lambda/S3 release workflows.
Catalog covered by the pricing architecture.
Reduced production time for 10,000+ product listings.
Reduced release cycles from multi-day deploys.
Technology Stack