Technical Deep Dive: Simbe Vision™ - Advanced Computer Vision Powering the Next Generation of Retail Operations

In today's competitive retail landscape, technological innovation is increasingly becoming the differentiator between market leaders and followers. At the forefront of this transformation is Simbe, and their proprietary computer vision system, Simbe Vision™, that is reshaping how retailers manage inventory and shelf analytics. As the only multimodal, enterprise-scale solution offering real-time, high-fidelity SKU-level visibility paired with prioritized next-best action recommendations, Simbe is transforming data into action that drives optimized execution across retail businesses. This blog post explores the technical underpinnings of Simbe Vision and how it differs from other retail computer vision implementations.

The Evolution of Simbe's Computer Vision Architecture

Simbe's computer vision system has undergone significant evolution over the years. The company adopted neural networks in production in mid-to-late 2017, Simbe pivoted to implementing Convolutional Neural Networks (CNNs) with anchor-based detection models. The architecture continued to evolve with the integration of CNNs paired with Long-Short Term Memory (LSTM) networks for optical character recognition (OCR) around 2019, while simultaneously transitioning to anchor-free detection models. Since 2022, Simbe's technology stack has incorporated transformer-based models for specific detection systems, showcasing the company's commitment to staying at the cutting edge of computer vision technology.

Multimodal Product Recognition

One capability that sets Simbe Vision apart is its hybrid approach to product recognition. Rather than relying solely on visual appearance, the system combines:

1. Visual embeddings for product identification across different lighting conditions and angles
2. Optical Character Recognition (OCR) to distinguish between similar products with minor packaging differences
3. Color and size analysis to further differentiate products
4. Tag assignment to handle cases where visual distinction alone isn't sufficient
5. Barcode scanning for precise product verification

This multimodal approach allows Simbe to handle one of retail's most challenging computer vision problems: distinguishing between visually similar products like original versus fat-free versions that may share nearly identical packaging. With 99% SKU-level identification accuracy (significantly outperforming the industry average of 85-90%), Simbe Vision leverages the largest dataset of its kind in retail—over 18 million images in our product recognition catalog—to deliver unmatched precision at enterprise scale.

Volumetric Detection for Pre-emptive Inventory Management

Perhaps one of the most innovative aspects of Simbe Vision is its volumetric detection capability for low-stock shelf detection. Simbe’s approach is focused on proactive inventory management:

• Using 3D data from depth cameras combined with back-of-shelf detection algorithms
• Defining virtual "boxes" where products should be located
• Calculating the percentage of empty space within these boxes
• Triggering low-stock alerts before items are completely out of stock

This pre-emptive approach to inventory management is a significant advancement over traditional binary in-stock/out-of-stock systems. By allowing retailers to replenish items before they're completely depleted, it reduces missed sales opportunities, prioritizes high-velocity, high-margin products, and creates a more dependable shopping experience. Unlike most competitive CV systems that rely solely on 2D imaging, Simbe's advanced depth sensors capture rich volumetric data without requiring disruptive additional lighting.

Massive-Scale Image Processing Infrastructure

In 2024 alone, Simbe processed over 14 billion shelf images and is projected to double that in 2025. To achieve this while delivering insights within minutes rather than hours or days, Simbe built a robust cloud infrastructure:

• Autoscaled compute resources on Google Cloud Platform (GCP)
• Workload distribution across a queue-based system
• Hybrid processing approach with some computation performed on the robot itself
• Edge computing capabilities that continue to expand with each robot generation
• Optimized processing that scans 5,400 items/hour with high-fidelity

This sophisticated infrastructure enables Simbe to provide near real-time inventory insights despite the enormous data volumes being processed. With long battery life, minimal onboard compute, and industry-low cloud processing costs, Simbe delivers enterprise-grade AI at an incredibly efficient price point compared to alternatives.

Balancing Precision and Recall in Simbe Vision

In computer vision and machine learning, precision and recall represent two fundamental metrics that evaluate model performance:

Precision measures the accuracy of positive predictions, when the system identifies something (like a product or empty shelf space), how often is it correct? It's calculated as: True Positives / (True Positives + False Positives). High precision means minimal false alarms.

Recall quantifies how completely the system identifies all relevant instances, what percentage of actual items does it successfully detect? It's calculated as: True Positives / (True Positives + False Negatives). High recall means few missed detections.

Simbe's Practical Approach

Simbe Vision takes a pragmatic approach to the inherent precision-recall trade-off when facing ambiguous visual data:

1. Prioritizing precision over recall - Simbe's system is designed to be more conservative, preferring to miss some detections (lower recall) rather than incorrectly identify products or shelf conditions (higher precision). This reflects an understanding that false positives can lead to wasted store associate time investigating non-existent issues.
2. Flexible accuracy definitions - Simbe has learned from real-world retail operations that strict technical definitions don't always align with practical business needs. Simbe makes thoughtful adjustments to the algorithms based on what actually helps store operations rather than pursuing theoretical perfection.
3. Practical application example - While technically a shelf with a single product facing remaining shouldn't trigger an out-of-stock alert, in practice, store operations benefit from knowing about this near-empty condition before it becomes a complete stockout. Simbe may raise an alert in such cases if it improves overall store team efficiency.

This precision-first philosophy with strategic exceptions demonstrates Simbe's focus on delivering actionable insights rather than optimizing for technical metrics alone. They understand that in retail environments, what matters most is providing practical information that helps store teams work more efficiently, even if that sometimes means departing from strict theoretical definitions.

Advanced Product Detection and Merchandising Analysis

Simbe Vision's capabilities go far beyond standard out-of-stock detection with sophisticated algorithms that identify common shelf execution issues:

• Plugs, Spreads, and Misplaced Product Recognition

The system can detect and address common shelf execution issues including product spreads (when products are distributed across more facings than assigned), plugs (incorrect products placed in a location), misplaced items, and missing price tags—all critical for maintaining planogram integrity and reducing inventory distortion.

• Similarity Detection

The technology differentiates between nearly identical products (e.g., flavor or size variants), preventing mis-merchandising and improving shelf accuracy. This is particularly valuable for retailers with large private label assortments or numerous product variants.

• Shelf-to-Stock Comparison

Simbe Vision cross-checks on-hand inventory with real-time shelf data to uncover hidden "plugs" that appear fully stocked at a glance, automating tasks that manual checks often miss and preventing phantom out-of-stocks for more accurate replenishment.

• Automated Shelf Tag Verification

Using barcode scanning, OCR, and proprietary machine learning, Simbe Vision detects mismatches between price tags and products, eliminating costly errors and ensuring pricing accuracy.

Continuous Product Catalog Management

One of the most challenging aspects of retail computer vision is maintaining an up-to-date product catalog. Simbe Vision handles this through:

• Automatic detection of new products not in the original training dataset
• An onboarding process that begins whenever data for a particular SKU isn't found
• Sampling images across many stores to build robust product representations
• Visual clustering to find optimal product representations

This system allows Simbe to continuously expand its product recognition capabilities without requiring manual retraining for each new product introduction.

Price and Promotion Verification

Beyond inventory management, Simbe Vision provides additional value through:

• OCR technology that reads prices with 99% accuracy
• Combined OCR and promotion classification to identify types of promotions on shelf tags
• Integration between visual and textual information for comprehensive shelf analysis

Specialized Approaches for Different Retail Environments

Simbe Vision incorporates specialized handling for different retail environments:

• Separate deep learning algorithms for scenes with doors, where depth cameras are less effective
• Configuration adjustments for fresh produce in different fixtures
• Lighting provided by the robot itself to create more controlled visual conditions

From Insight to Action: Next-Best Recommendations

One of Simbe Vision's most significant differentiators is its ability to go beyond simply surfacing problems to recommending the next-best actions based on business impact. While other store intelligence solutions surface issues but leave store teams to determine how to resolve them, Simbe's Store Intelligence™ Platform delivers SKU-level insights and prioritizes next-best actions across inventory, pricing, and merchandising.

This capability helps retailers shift from asking "What's happening?" to knowing "What should I do about it?" This leap supports predictive, self-learning stores that continuously improve performance over time. Store teams can quickly prioritize what matters most, shifting from reactive issue handling to proactive execution that improves labor efficiency and drives real results.

Future Directions

As retail computer vision continues to evolve, Simbe Vision is expanding its capabilities through:

• Increasing edge computing capabilities
  • Edge computing brings computation closer to data sources and end users, reducing latency and bandwidth usage. The value this drives includes faster response times for applications requiring real-time processing like autonomous vehicles and smart manufacturing systems. By processing data locally rather than sending everything to the cloud, edge computing significantly reduces bandwidth requirements and costs, which is particularly valuable for IoT deployments with limited connectivity. Additionally, it enhances privacy and security by keeping sensitive data local rather than transmitting it across networks. This distributed approach also improves system resilience by reducing dependence on central cloud infrastructure.
• Exploring more transformer-based models
  • Transformer-based models have revolutionized machine learning with their attention mechanisms. Their value comes from superior performance on complex language and vision tasks compared to previous architectures. They excel at capturing long-range dependencies in data and can be efficiently parallelized for faster training. The flexibility of transformers allows them to work across text, images, audio, and video, making them versatile building blocks for various AI applications. Their proven scalability means performance often improves with more parameters and training data, creating a clear path to more capable systems.
• Selectively leveraging Large Language Models (LLMs) for certain tasks
  • Strategic deployment of LLMs provides value by applying these powerful tools precisely where they offer the greatest benefit rather than using them indiscriminately. This approach optimizes cost efficiency, as LLMs' computational demands and associated expenses can be directed only to tasks where their capabilities are truly needed. Selective LLM use allows organizations to maintain appropriate human involvement in sensitive decisions while still benefiting from AI assistance, addressing ethical concerns around full automation of consequential processes. This targeted approach also helps manage latency issues by reserving high-latency LLM operations for non-time-critical components of applications.
• Continued expansion of multimodal intelligence capabilities
  • Expanding multimodal intelligence delivers value by enabling AI systems to process and synthesize information across different formats (text, images, audio, video) simultaneously, similar to human perception. This creates more natural and intuitive human-computer interactions, as users can communicate using their preferred modalities rather than adapting to the system's limitations. Multimodal systems extract richer insights by analyzing correlations between different data types that might be missed in unimodal analysis. They're also more accessible to diverse users, including those with disabilities, as they provide multiple ways to interact with technology. Finally, they tend to be more robust since they can fall back on alternative modalities when one data stream is compromised or unavailable.

Competitive Advantages of Simbe Vision

When compared to alternatives in the market, Simbe Vision offers several crucial advantages:

• Industry-Leading Accuracy at Scale

With 99% SKU-level identification accuracy and shelf condition recall, Simbe significantly outperforms typical CV systems that average just 85-90% accuracy and 75-85% recall. Legacy inventory methods like handheld audits and barcode scans average around 65% accuracy. This combination of unmatched precision, speed, and coverage enables faster, smarter retail decisions.

• Fixed Cameras Without Planogram Dependency

Unlike most fixed-camera systems that rely on rigid planogram data, Simbe's Tally Spot uses real-time insights from Tally's daily store traversals. This removes the need for store teams to follow rigid workflows and delivers more adaptive, accurate shelf intelligence.

• Multimodal Coverage

Simbe Vision offers comprehensive coverage through mobile robots, fixed cameras, and integration with RFID and other data sources, unlike single-modality alternatives.

• Customer-Proven Impact

Simbe Vision has been validated by leading global retailers across major chains like Schnuck Markets, Wakefern, and SpartanNash, with demonstrated improvements in pricing accuracy, inventory management, and operational efficiency.

Conclusion

Simbe Vision prioritizes comprehensive shelf intelligence for inventory management, planogram compliance, and pricing accuracy. By combining advanced neural network architectures with practical retail domain knowledge, Simbe has created a computer vision system uniquely suited to the challenges of modern retail operations.

The continued evolution of Simbe Vision illustrates how specialized computer vision approaches can deliver transformative value when tailored to specific industry needs, rather than attempting to create generic solutions. As retail continues to evolve, technologies like Simbe Vision will increasingly become essential tools for maintaining competitive advantage in an increasingly data-driven industry.