This case study shows how SBL turns operational complexity into a governed digital operating model where workflow, data, quality and decisions can be managed together.
The institutional challenge
- Mixed waste visibility: Distinguishing between plastic, bio, and metal in cluttered environments.
- Manual effort costs: High local labor rates made large-scale manual labeling financially impossible.
- No off-the-shelf solution: Unique urban environments required a custom-built taxonomy for accuracy.
What SBL engineered
- Built a managed AI operations model with controlled intake, workflow routing, quality review and delivery governance.
- Combined automation with human oversight to handle scale, variation and exceptions.
- Used operational dashboards and repeatable quality controls to make the workflow measurable and improvable.
- Consult & Align: Defining custom waste taxonomies (plastic, bio, metal, etc.).
- Workflow Setup: Rapid PoC setup to validate real-time annotation requirements.
How the work should be understood
SBL's role is not limited to executing a task. The value comes from understanding the operating workflow, engineering the data and automation layer, applying governance controls, and helping the institution move from manual dependency to intelligent, measurable operations.
