AI-Powered Retail Annotation

3.5 million retail images annotated in under five months at 98%+ accuracy. A UK-based retail AI company building computer vision systems for shelf visibility and product placement analysis required large-scale annotation support to train and improve its machine learning models. Existing freelancer-led workflows struggled to maintain consistency across rapidly growing image datasets collected from thousands of retail environments.

Dedicated annotation operations processed 3.5 million retail images, improving computer vision training speed, consistency and retail AI scalability.

Service: Generative AI

3.5M+: Images annotated in <5 months
98%+: Accuracy maintained
−58%: Cost vs previous annotation model

Challenge

The client needed to scale annotation operations without compromising quality, turnaround time or operational cost. Image datasets varied significantly across store layouts, lighting conditions and product categories, making consistency difficult to maintain through fragmented freelancer networks. Expanding internal annotation teams also introduced rising overhead and training complexity across custom annotation workflows.

Approach

SBL Infotech built and deployed a dedicated annotation workforce of 80+ trained specialists operating exclusively on the client’s custom tools and workflows. The engagement supported multiple annotation formats including bounding boxes, polygons and classification-based labeling across high-volume retail image datasets. A 100% quality review framework led by subject-matter experts ensured accuracy remained above 98% across all deliverables. Direct API integration with the client’s systems automated submission, tracking and reporting workflows while continuous monitoring and feedback loops maintained operational consistency throughout the engagement.

Outcome

The client replaced fragmented freelancer operations with a scalable annotation pipeline capable of supporting long-term AI development across expanding retail datasets. Faster annotation throughput accelerated model training and deployment cycles while significantly reducing operational costs and internal coordination overhead. The engagement established a repeatable computer vision annotation framework that improved data reliability, simplified workforce scaling and removed a major operational bottleneck from the client’s AI roadmap.

IX Case studies

More from the field

US university research initiative

Reliving History with Geospatial Intelligence

260 years of fragmented historical maps transformed into a georeferenced spatial database for anthropological and land-use analysis. A prominent US university needed to study the historical evolution of Uxeau, France, across multiple centuries of land ownership, taxation, and agricultural activity. The research depended on digitising and harmonising vintage maps dating back to 1759 — each with different scales, formats, and levels of degradation — into a single spatially accurate GIS environment suitable for comparative analysis. 260+ years of historical mapping digitised and layered. Lambert II precision georeferencing using Esri GIS tools. Multi-era land parcel and feature extraction delivered at scale.

"Historical GIS digitisation transformed fragmented archival maps into a searchable spatial database, accelerating anthropological research and long-term land-use analysis. "

US radiology AI company

AI-Powered CT Scan Annotation

30,000+ CT scans annotated at 98.9% segmentation accuracy for AI-driven radiology models. A US-based MedTech AI company developing radiology models for tumour detection and analysis required clinically precise annotation support to accelerate model training and validation. Existing workflows faced rising costs, limited access to qualified medical annotators and growing compliance pressure around handling sensitive patient imaging data.

HIPAA-compliant medical annotation workflows improved radiology AI accuracy, accelerated tumour detection model training and reduced operational costs significantly.

A US-based mortgage company

Mortgage Foreclosure Data Management

40%+ faster foreclosure data processing with 50% higher accuracy across multi-county property records. A US-based mortgage data company managing property intelligence across more than 155 million properties and 3,000+ counties required a scalable operational model for foreclosure data collection and processing. Their existing workflows relied heavily on manual back-office operations, creating delays, inconsistencies and rising operational overhead across fragmented government data sources.

Standardised foreclosure processing improved nationwide property data accuracy, reduced turnaround times and created a scalable mortgage intelligence operations framework.