<12 months
built a scalable data, analytics, and ML capability.
The Challenge
CtrlChain’s ambition to use ML for smarter matching was blocked by the fundamental challenge of building an AI capability from scratch:
- Missing Foundation: No scalable data platform existed to gather and utilize historic operational data.
- Strategy Gap: Lack of clarity on which use cases to prioritize first (e.g., pricing vs. recommendation)
- Talent/Team: Undefined hiring needs for the required data, analytics, and ML engineering skills.
Our Approach
Following an Design Sprint, Enjins co-developed a future-proof Data Analytics Platform alongside the first high-value use case: FTL Spot Pricing. This approach ensured:
- Prioritization: Focused on the achievable, high-impact FTL Spot Price Model to build foundations first.
- Architecture: Designed a platform (3-layered schema) to scale easily on data volume and streamline analytics for ML deployment (microservices).
- Team Building: Defined and supported the hiring plan (Data Engineer, Analytics Engineer) to ensure internal ownership and long-term capability.
"This collaborative effort... has proven as a robust initiative to minimize wastage within the logistics sector."
Rick van Elk
VP Technology at CrtlChain
