Connecting the physical realities of a shop floor—inventory misplaced on the wrong shelf, queues building at the register, unpredictable foot traffic—to a digital system requires more than modern point-of-sale software. It requires translating physical movement into structured data without disrupting the people working in the store.
The reality of modern infrastructure, unpacked.
Running a physical retail environment involves managing constant entropy. Inventory arrives, is moved, misplaced, purchased, or stolen. Staff need to be allocated where traffic is highest, but traffic patterns change by the hour. All of this happens in real time, yet the systems used to manage it often rely on batch processing or manual counting—meaning the store is always being managed based on what happened yesterday.
The gap is usually between the transactional data (what was sold) and the spatial data (what is actually on the shelf or happening in the aisle right now). Modern POS systems handle the transaction perfectly, but the inventory record assumes perfection between the loading bay and the register. When an item is picked up by a customer and put back in the wrong aisle, the system still registers it as available. Bridging that gap requires computer vision, sensor networks, and edge computing that traditional retail stacks were not designed to support.
The burden falls entirely on floor staff and store managers. A manager spending hours conducting manual cycle counts instead of coaching their team. A floor associate trying to help a customer, relying on a stock system that says 'two available' when the physical shelf is empty. These are not just administrative inefficiencies; they erode staff morale, create unnecessary friction, and directly damage the customer's trust in the brand.
We target specific workflows where manual effort meets its ceiling, delivering measurable, high-leverage outcomes.
Manual cycle counts are time-consuming and prone to human error. By the time a discrepancy is discovered, the resulting stockout has already cost the store sales and frustrated customers.
Retailers possess granular data on e-commerce browsing, but physical stores remain a black box. Knowing what was bought does not explain what was looked at, picked up, and ultimately abandoned.
Updating paper shelf-edge labels across a large format store requires hours of manual labour. Discrepancies between the shelf price and the POS create friction at checkout and regulatory compliance risks.
Traditional CCTV requires constant human monitoring and usually only serves as retroactive evidence. It does not actively identify or prevent shrink as it occurs on the shop floor.
Refrigeration failures, HVAC faults, or energy spikes are often discovered too late, resulting in spoiled perishable inventory, uncomfortable shopping environments, or compliance breaches.
Outcomes in the reader's language, focused on actual usage.
Camera and sensor infrastructure that monitors shelf states, detects out-of-stocks, and flags planogram non-compliance, triggering replenishment alerts automatically.
Edge-processed foot traffic analysis that maps store heatmaps, zone conversions, and queue lengths without capturing or storing any personally identifiable information.
Wireless e-ink display networks connected directly to the retailer's ERP and pricing engine, ensuring pricing consistency between the shelf and the register.
Sensor networks covering temperature, humidity, and energy consumption across refrigeration and HVAC systems, with predictive maintenance alerting.
Vision models trained to recognise specific movement signatures associated with theft, integrated with staff communication tools for discreet intervention.
A single, role-based interface combining inventory alerts, traffic data, and equipment status—giving store managers one place to understand current operational realities.
Computer vision in a busy retail environment has to account for occlusions, varying lighting, and dense crowds. It is not perfect. We design systems to flag exceptions for human review rather than making autonomous decisions that could disrupt the shopping experience or wrongly accuse a customer. AI in physical retail supplements the store team; it does not replace the need for human judgment on the shop floor. The most frequent and valid concern regarding retail tech is shopper privacy. We build spatial analytics and vision systems using edge processing—meaning the video feed is converted to anonymous coordinate data locally, and the video itself is immediately discarded. We do not use facial recognition to identify individuals, nor do we track personally identifiable information without explicit, opt-in consent (such as through a loyalty app). We ensure the architecture complies strictly with GDPR and CCPA requirements.
A vision model analyses images from shelf-facing cameras to recognise which products are present, their orientation, and how many remain facing outward. It compares this against the expected planogram and updates the inventory record or alerts staff to restock a specific aisle.
A forecasting model trained on historical sales, local event calendars, weather data, and foot traffic patterns provides a probability-weighted view of expected volume. This allows store managers to align shift schedules with actual predicted demand rather than relying purely on last year's figures.
Rather than requiring a security guard to watch dozens of camera feeds simultaneously, a model monitors for specific physical movement patterns—such as sweeping multiple items into a bag at once—and surfaces only the flagged video snippets for human review.
We map the physical workflow of the store before we propose any technology. A system that requires floor staff to change their natural behaviour will ultimately be bypassed.
We spend time understanding your current operations—how inventory is unloaded, how staff manage restocks during peak hours, and where the daily friction actually lives. Technology built in a lab often fails in a stockroom. We design around the reality of your physical space.
A new sensor network is useless if it requires staff to check a completely separate application. We scope the integration with your existing POS, ERP, and inventory management systems as the foundation of the build, ensuring new data flows into the tools your team already uses.
We do not pilot exclusively in pristine flagship stores. We deploy initial technology in locations with representative challenges—older layouts, typical staffing levels, and standard network constraints. If the technology cannot survive there, it cannot scale.
We measure success not by technical uptime, but by operational outcomes: hours saved on cycle counts, reduction in out-of-stock incidents, and decreases in shrinkage. The technology is only marked as successful when the store manager confirms it has made the store easier to run.
Measured by operational outcomes, not just technical uptime.
0%
Inventory accuracy
achieved using automated computer vision tracking
< 0 min
Store-wide price updates
following implementation of ESL infrastructure
~0%
Reduction in stockouts
grocery chain using automated replenishment triggers
Quiet, honest, and specific results.
A regional grocery chain was running manual inventory counts that took roughly 40 hours per week per store, with an accuracy rate of around 70%. Stockout complaints were increasing, and perishable items were frequently expiring on shelves because staff were unable to rotate stock efficiently.
Inventory accuracy improved to approximately 98%. Stockouts were reduced by around 75%. The 40 weekly hours previously spent on manual counting were reallocated to customer-facing tasks, and perishable waste decreased noticeably over the following two quarters.
A large-format electronics retailer wanted to reduce the time staff spent on manual price changes and address discrepancies between shelf prices and the POS, which were causing friction at the register and drawing regulatory scrutiny.
Store-wide price updates that previously took several staff members a full morning now complete in under five minutes. Pricing discrepancies at the register dropped to near zero in the first month, entirely eliminating the associated customer friction.
A fashion retailer had robust e-commerce data but no visibility into physical store performance beyond final sales. They could not determine whether poor sales of a new line were due to low foot traffic, poor placement, or lack of customer interest after viewing.
The retailer identified that a heavily promoted line was placed in a low-traffic blind spot. After adjusting the store layout based on the heatmaps, engagement with the new line increased by roughly 40%, directly correlating with a measurable lift in sales for that specific category.
Fresh produce monitoring, expiration tracking, temperature compliance for refrigeration units, and automated reordering to prevent food waste without requiring staff to manually check every aisle.
A two-week review of your current operations, existing hardware, network infrastructure, and where the measurable operational gaps are. We produce a clear picture of what is technically feasible and what is worth addressing.
A focused 6–8 week build covering one specific capability—such as spatial analytics or environmental monitoring—in one or two representative stores. We define success metrics upfront and measure against them rigorously.
A 12–16 week deployment across multiple locations. We manage the integration with central systems, coordinate hardware installation, and provide the training required for store teams to adopt the new workflows.
Continued involvement after launch—retraining vision models as store layouts change, adjusting alert thresholds, and expanding the sensor network as the results justify further investment.
Enterprise-grade security embedded at the core.
Enterprise-grade controls, rigorous compliance baselines, and delivery discipline woven into the architecture from day zero.
Spatial analytics and computer vision systems process video feeds locally on edge devices. Video is converted instantly into anonymous coordinate data, and the raw footage is discarded. No personally identifiable information (PII) is captured or stored.
Sensor networks operate on segregated VLANs, distinct from the store's primary POS or corporate networks. All telemetry data in transit is encrypted using TLS, and devices are authenticated using unique cryptographic certificates.
Data collection architectures are mapped strictly to GDPR and CCPA requirements. Automated monitoring for temperature-sensitive products maintains immutable logs to satisfy food safety and pharmaceutical compliance inspections.
Adhering to the highest standards of security and regulatory compliance.
Our foundational technology stack is designed around principles of immutability, deterministic performance, and zero-trust security. We deploy modern, enterprise-grade tooling to ensure every architecture we deliver is robust and extensible.
Visual analysis infrastructure for inventory tracking, spatial analytics, and anomaly detection
Everything you need to know about partnering with us and our engineering standards.
Every retailer is dealing with a unique mix of legacy systems, physical constraints, and operational bottlenecks. Whether you are looking to automate inventory, understand store traffic, or simply modernise your pricing infrastructure, we are glad to hear where you are. No presentation. Just a conversation about what you are working through.