When a global tobacco and lifestyle brand sought a solution to analyze how pricing strategies would impact market share and volume for one of their most iconic products, our team at Thoucentric was tasked with building a sophisticated ML-powered pricing solution. This case study details how we built an enterprise-grade pricing intelligence platform that could transform raw market data into actionable pricing strategies across 42 global markets, each with unique competitive dynamics.

The Challenge

The main challenge the client faced was to optimize pricing strategies across diverse global markets while balancing profitability with market share. Their existing process relied heavily on manual analysis and disconnected spreadsheets, making it difficult to:

  • Ensure consistency in pricing decisions across regions
  • Predict how price changes would affect market demand and competitor responses
  • Quickly adapt to changing market conditions
  • Leverage historical data for better forecasting

They needed a centralized, data-driven solution that could provide real-time insights to pricing strategists worldwide.

Solution Architecture

We designed a comprehensive pricing intelligence platform with the following components:

  1. Backend API Layer: RESTful services built with FastAPI to power pricing calculations, data retrieval, and model interfacing
  2. Real-time Communication: WebSocket implementation for instant feedback loops and improved UX.
  3. ML Model Pipeline: MLFlow for model versioning, deployment, and inference
  4. Containerized Deployment: Docker + Kubernetes for scale and reliability
  5. CI/CD Integration: Jenkins-based deployment pipelines

pricing-platform.png

Implementation

As the lead backend engineer, I was responsible for translating the pricing domain into a robust backend system, designing and implementing the MLOps pipelines, and making it all production-ready.

FastAPI Backend

I architected a modular API system adhering to OpenAPI standards, including:

  • Well-structured endpoints for pricing analysis, data ingestion, and simulation
  • Query performance optimization for large datasets
  • Thorough validation and permission checks to ensure data and access integrity

MLFlow Integration

We integrated market-specific models using MLFlow and designed a dynamic model loader with an inference wrapper in FastAPI, enabling seamless model deployment and prediction serving.

Some specific highlights of the system were:

  • Zero-downtime deployments: Data scientists could update market models independently without coordinating with engineering or causing API disruptions
  • Reliability at scale: The real value became evident during the quarterly pricing updates when the platform handled increased traffic while maintaining consistent response times. One pricing analyst noted, “This has cut our pricing update process from days to hours.”

Containerization and Deployment

The entire platform was built on top of AWS Elastic Kubernetes Service (EKS), some key features of the deployment pipelines were:

  • Consistent deployments: We established a CI/CD pipeline with Jenkins that standardized how components were built, tested, and deployed
  • Better resource utilization: Basic autoscaling configurations helped optimize cloud resource usage, reducing costs by about 20%
  • Improved observability: Centralized logging with ELK stack and Grafana dashboards made troubleshooting more efficient

Results and Impact

  • 60% Faster Time-to-Market: Pricing updates rolled out with significantly reduced lead time
  • Centralized Collaboration: Teams across regions operated from unified systems and data models
  • Smarter Decisions: ML-based forecasting replaced manual guesswork

Key Learnings

  1. Production ML at scale requires more than just model accuracy, it demands thoughtful system design that considers data drift, inference latency, and failure scenarios
  2. MLOps needs robust tooling because successful ML deployments require data version control, experiment tracking, and automated deployment pipelines to maintain production reliability
  3. Scalability is more than infrastructure, it’s also about API performance and how to structure your database
  4. Cross-functional collaboration between data science and engineering teams required establishing shared vocabulary and clear interfaces

Tech Stack

  • Backend: Python, FastAPI
  • MLOps: MLFlow, AirFlow, DVC
  • Infrastructure: Docker, Kubernetes, Terraform, AWS EKS, Hashicorp Vault
  • Monitoring: Prometheus, Grafana, ELK Stack
  • CI/CD: Jenkins
  • Database: PostgreSQL
  • Frontend Interface: React

Conclusion

This project was a defining moment in my journey as a backend and MLOps engineer. From building high-performance APIs to orchestrating ML pipelines and real-time systems, I was able to contribute to a mission-critical solution with real business impact. The architectural patterns and deployment pipelines established here continue to shape how I approach enterprise-grade ML products today.