The Analysis Pillar - Why Fragmented Architectures Cripple Insight)
Savi
Your video data is now accessible (Pillar 1) and connected to your business systems (Pillar 2). The final pillar, Analysis, is about transforming that raw video footage itself into actionable intelligence using the power of Artificial Intelligence (AI) and Computer Vision. AI holds immense potential to revolutionize how businesses understand operations, customer behavior, and compliance – moving far beyond simple recording.
The Evolution of Video AI: From Narrow Tasks to Broad Understanding
Traditional computer vision often involved models trained for one specific, narrow task (e.g., detecting only a specific uniform color). These models were often brittle – requiring costly retraining if anything changed – and couldn't grasp the bigger picture.
Modern AI, leveraging concepts from Large Language Models (LLMs) and Vision Language Models (VLMs), operates differently. It can interpret video more like a human does – recognizing objects, actions, and context simultaneously (Is the floor clean? Is an employee on their phone? How many people are in line?). This advanced AI analyzes the entire scene holistically and doesn't "miss" details like manual observation can.
The Analysis Barrier: Why Fragmentation Kills AI Potential
The promise of this powerful AI is enticing, but realizing it across a multi-site enterprise runs headlong into a fundamental barrier: Analysis must happen where the data is stored.
If your video data architecture is fragmented – split between edge devices, on-premise NVRs, and perhaps separate cloud archives – applying consistent, sophisticated AI analysis across your entire operation becomes practically impossible:
Inconsistent Application: How do you ensure the same AI model for detecting queue lengths runs identically across hundreds of sites with varying local hardware?
Correlating Insights: How do you analyze a complete customer journey if footage from different zones resides in different storage locations?
Data Gravity: Moving massive amounts of video data from distributed locations to a central point just for analysis is often technically infeasible and prohibitively expensive.
The Twin Challenges: Compute Costs & The Hardware Upgrade Trap
Beyond fragmentation, two major hurdles prevent widespread adoption of advanced AI with traditional or hybrid systems:
The Compute Cost Equation: Running sophisticated AI models requires significant computational power and the cost isn't uniform, depending heavily on:
Breadth: Analyzing many different things simultaneously (like modern AI does) requires more power than a narrow task.
Frequency/Velocity: Analyzing every single frame (needed for accurate tracking like customer flow) is vastly more computationally intensive (and thus costly) than analyzing periodically (e.g., checking site cleanliness every 15 minutes). Trying to run broad analysis at high frequency across thousands of cameras using generic cloud AI tools without optimization quickly becomes uneconomical.
The Hardware Upgrade Trap: Perhaps the biggest hidden cost in non-unified systems is needing new hardware at every site to enable new AI features. Want to add drive-thru timing analysis? That might require upgrading NVRs or installing dedicated AI appliances chain-wide. Want to later add slip-and-fall detection? That could mean another costly hardware rollout. For a large chain, this can translate into millions of dollars and logistical nightmares just to introduce a single new analytic, stifling innovation.
The 100% Cloud Advantage: Enabling Affordable, Scalable AI
A 100% cloud-native architecture, where all video data is unified in a central platform, is the key to overcoming these barriers and unlocking the true potential of AI analysis affordably and scalably:
Unified Data Access: With all video in one logical place, sophisticated AI models can be applied consistently across any camera, site, or time period. Enterprise-wide analysis becomes feasible.
Optimized Compute Management: Cloud platforms allow for intelligent optimization of AI workloads.2 They can efficiently run high-frequency analysis (like customer flow tracking) using specialized models/processors where needed, while executing broader, lower-frequency analysis (like compliance checks) cost-effectively. This makes advanced AI economical.
Multi-Vendor, Best-of-Breed AI (Open AI Model): A cloud platform can integrate with multiple specialized AI vendors simultaneously. This allows leveraging the best available AI for each specific task (e.g., one vendor for customer journey analytics, another for cleanliness checks) delivered seamlessly to the end-user. (This reflects Savi's Open AI approach). Customers benefit from cutting-edge innovation from various sources.
AI Delivered via Software: New AI capabilities and improvements are rolled out through cloud software updates – no site visits or hardware replacements needed. This eliminates the hardware upgrade trap and drastically lowers the cost and complexity of innovation.
Future-Proofing: Easily adopt and test new AI tools and capabilities as they emerge and prove their value (accuracy, affordability, ROI), ensuring your investment keeps pace with the rapid rate of technological change.
Focusing AI on Impact: Accuracy, Affordability, and ROI
While AI possibilities seem endless, the most effective approach focuses on applications that are accurate, affordable, and deliver clear Return on Investment (ROI) today. Technology for technology's sake often fails (like the CEO's order accuracy example). The goal is to leverage AI to solve tangible business problems that significantly impact the bottom line and customer/employee experience.
Real-World Examples (Enabled by Cloud Architecture):
Customer Flow Analysis: Accurately tracking guest journeys (e.g., drive-thru timings, time-to-service in line) requires high-frequency analysis of every frame. Cloud platforms optimize the compute for this specific task, providing crucial insights into service bottlenecks that impact customer satisfaction (a key driver of negative reviews). Trying to do this with site-based hardware would be extremely costly to deploy and maintain across many locations.
Site Compliance Checks (SiteCheck): Evaluating store cleanliness, organization, staff uniform adherence, or attentiveness (e.g., not on phone) requires broad contextual analysis but can be done periodically (lower frequency). A cloud platform can run these checks efficiently across all sites using specialized AI models, providing compliance scores and actionable insights for operational improvement without needing dedicated site hardware for this task.
Rapid Deployment of New AI: A pharmacy chain wants to implement AI-powered spill detection. With a unified cloud platform, this is primarily a software deployment managed centrally. With a fragmented or on-prem system, it could involve installing new hardware or complex software updates at hundreds of individual pharmacies.
By leveraging a 100% cloud architecture, multi-site enterprises can finally harness the power of advanced AI analysis – not just as a technical capability, but as a practical, affordable, and scalable engine for driving efficiency, improving customer experience, and reducing loss. This sets the stage for quantifiable business impact.
