I would love to give you a live demo or walk you through the platform, but the system is internal and only accessible to people at Animalia and Nortura. The data in the screenshots below has been blurred, since it should only be accessed by people at Nortura and Animalia. Feel free to reach out if you have questions about the technical details.
Kalkyle – Slaughter Analytics Platform
Backend platform for analyzing carcass yields, cutting patterns, and production efficiency — used by production specialists at Animalia and multiple Nortura slaughterhouses.
Role
Backend Developer & Data Engineer
Organization
Animalia
Used by
Animalia and Nortura slaughterhouses
Technologies
Python, FastAPI, SQL Server, Azure, Parquet, Docker
The Problem
Meat processors generate large amounts of production data, but analyzing how carcasses are transformed into products can be challenging. Different slaughterhouses use different cutting patterns, products, and production processes, making it difficult to compare performance and identify opportunities for improvement. Animalia wanted a platform that could automatically process slaughter data and provide detailed analytics on yields, weights, and processing efficiency.
My Contribution
I designed and implemented the backend platform responsible for data ingestion, business logic, caching, and API delivery. The system automatically collects production data, transforms it into a hierarchical product structure, and exposes analytics through a REST API used by the frontend dashboard.
Product Hierarchy Modelling
One of the key challenges was modelling how carcasses are broken down into cuts and final products. I developed a configurable hierarchy system that represents relationships between carcasses, cuts, and processed products. This allows users to analyze yields and weights at multiple levels of the production chain.
Analytics Dashboard
The processed data is presented through interactive dashboards showing:
- Average yields and weights
- Product distributions
- Processing statistics
- Variation between cutting patterns
- Exportable reports
The platform enables production specialists to quickly understand how raw material is converted into finished products.
Configurable Rules Engine
To avoid hardcoding slaughterhouse-specific business logic, I developed a JSON-driven rules engine that defines product relationships and cutting patterns. This enables domain experts to modify production definitions without requiring changes to the underlying application code.
Technical Architecture
The backend consists of a modular architecture designed for scalability and maintainability. A JSON-based rules engine separates business logic from application code, allowing domain experts to update cutting pattern definitions without requiring software changes.
Impact
The platform is actively used by production specialists at Animalia and multiple Nortura slaughterhouses to analyze production performance and carcass utilization. By automating data processing and centralizing analytics, the system replaced manual reporting with consistent, near-real-time KPIs accessible across facilities.
Key Contributions
- Designed and implemented the backend architecture.
- Built a configurable JSON-driven rules engine.
- Developed data ingestion and caching pipelines.
- Implemented analytics APIs used by the frontend dashboard.
- Collaborated with domain experts to model slaughterhouse business logic.
- Deployed and maintained a production system used by industry stakeholders.