Turning mission-critical data into waste intelligence

The mission: Build a cleaner planet with Vision-led AI insights

A fast-growing analytics company in the recycling and environmental space set out to push the boundaries of machine learning for waste recognition. They aimed to use computer vision to identify, analyze, and diagnose waste streams across 89 detailed material categories, enabling smarter, greener decisions that meet regulatory compliance. In a high-risk, high-impact domain where mislabeling could result in flawed analytics, environmental misreporting, or compliance penalties, data quality was mission-critical.

The challenge: Scaling with accuracy in a regulated context

The model required a massive volume of labeled visual data, with a monthly target of 1 million annotations. But speed came at a cost: inconsistent quality, limited in-house expertise, and growing pressure to meet regulatory compliance. The complexity of their domain in visual waste recognition introduced specific bottlenecks.

Key obstacles:

• Lack of specialized domain expertise in annotating and segmenting complex visuals across 89 waste categories
• Inability to strictly meet tight turnaround windows, with up to 1 million image annotations needed per month
• Difficulty ensuring high annotation accuracy, especially for edge cases and regulated classifications
• Strict compliance requirements, demanding traceability, auditability, and high-confidence outputs
• Poorly labeled data impacted the accuracy of recognition models, weakened analytics across diverse waste streams, and stalled their models advancement

The goal:

Deploy a data workforce of specialized knowledge and operational rigor to keep pace with growth.

The solution: Expert-led labeling with built-in risk management

To address these domain-specific challenges, we designed a bespoke data labeling solution rooted in three core pillars: expertise, process control, and speed.

Our approach:

1. Curated a specialized labeling workforce with domain-trained annotators knowledgeable in waste material standards, recyclability classifications, and edge-case scenarios
2. Implemented an annotation solution, integrating version control, labeling protocols, and continuous QA to ensure confidence and traceability at scale
3. Cut start-up time with a data pipeline to accelerate labeling process
4. Integrated human-in-the-loop (HITL) review cycles to handle low-confidence images and ambiguous cases
5. Delivered AI-ready datasets with structured metadata, enabling clean training inputs for models tasked with detection, sorting, and compliance reporting

The results: High fidelity data at speed

• Target of 1 million annotations per month achieved
• Achieved over 97% model improvement by expanding the dataset to 89 waste categories, enabling granular insights that optimize recycling workflows
• Data accuracy and consistency scores peaked at 97%
• Start-up time reduced by 93%, from 2-3 weeks to 24 hours without compromising data quality
• Data auditability and compliance adherence

By combining deep domain knowledge with operational agility, we helped the client bridge the gap between data reality and AI environmental impact. This enabled faster innovation in a field where accuracy is of utmost importance.