Taking Carbon Footprint Management from PoC to Scalable AI-Powered SaaS.

Challenges

A visionary start-up consulting agency built an early-stage carbon footprint management tool for the food industry that had passed the PoC stage and was gaining market traction.
Goal one: design and implement the architecture for commercial cloud SaaS delivery — multi-tenant, automated ingestion, scalability, security and data governance.
Goal two: elevate the business offerings to the next level by employing and demonstrating the usefulness of AI.

Facilitated workshops to tailor strategy, architecture and solution design to overall vision.
Performed analyses and assessments on data quality and volumes.
Developed data strategy outlining principles, priorities, budget estimates and future roadmap.
Designed data governance processes to ensure data quality across disparate client environments.
Designed scalable cloud architecture to support SaaS delivery.
Designed relational database schemas optimized for central master data management.
Developed emissions calculation engine as a microservice, decoupled and reusable across the suite.
Developed upload helper tool for bulk data ingestion with validation and mapping to central master data model.
Developed Recommender System to suggest alternative ingredients with lower carbon footprints — deployed as an API microservice.
Developed interactive tool for product management, allowing users to swap ingredients and assess carbon impact.
Developed forecasting module to track changes against Net Zero goals.
Developed dashboard to provide key emissions insights for stakeholders and executives.

Cloud architecture to support AI-powered SaaS delivery.
AI-powered, forward-looking carbon footprint management — enhancing a unique value proposition in the market.
Data strategy to guide future development and funding.

Technologies & methods

Cloud Architecture Azure Recommender Systems API Microservices Data Strategy Data Governance Data Engineering Data Ingestion AI