SEE. UNDERSTAND. DECIDE. ACT. This is engineering, not a tagline.
Every capability described elsewhere on this site — drive-thru timers, PPE compliance, shelf gaps, self-checkout shrink — is the output of a specific, purpose-built model running against a specific operational signal. This section documents that layer directly: the algorithm library, the engineering methodology behind it, and applied deep-dives into how individual capabilities were actually built.
The Engineering Framework
Four Stages, Applied at the Model Level
Not a marketing sequence — the actual data flow every deployed algorithm moves through, from camera frame to system action.
SEE
Cameras and sensors already on-site become model input — no new hardware required to start.
UNDERSTAND
Purpose-built detection and tracking models turn raw frames into structured, labeled events.
DECIDE
Structured events are checked against defined operational criteria — SOP thresholds, POS records, PPE requirements.
ACT
A qualifying event routes to an alert, a evidence clip, or a system integration — automatically, not on a review cycle.
The Algorithm Library
75+ Algorithms Across 6 Intelligence Domains
Every production algorithm is scoped to one of six domains of physical operations. Each card links to the specific, named capabilities inside that domain.
PEOPLE
Footfall, Demographics & Customer Bounce
Track visitor traffic, age/gender estimation, table occupancy dwell times, and flag customer bounce (customers exiting without purchase).
PRODUCTS
Shelf Intelligence, OOS & Stock Count
Real-time empty shelf detection, mobile inventory stock verification, out-of-stock alerts, and planogram compliance.
PROCESSES
Handwash SOP & Machine Utilization
Enforce handwash sanitization SOPs, vessel cleaning protocols, equipment maintenance, and 24/7 machine active/idle state tracking.
TRANSACTIONS
POS Sync, UPI Verification & Void Alerts
Detect food prepared/served without POS bill generation, reconcile UPI digital payments, and monitor unauthorized cash till access.
EQUIPMENT
Drive-Thru Timers & Machine States
Track end-to-end drive-thru vehicle journey, order serving performance, conveyor speed, and equipment maintenance status.
ENVIRONMENT
6-Point PPE Safety & Intrusion Alerts
Audit 6-point PPE compliance (Helmet, Goggles, Mask, Gloves, Uniform, Shoes), spill hazards, un-mopped floors, and non-business hour intrusion.
How We Build
Five Engineering Principles Behind Every Model
These aren't abstractions — they're the design decisions that shape how each of the 75+ algorithms is trained, deployed, and run.
Edge or Cloud GPU Inference
Inference runs on GPU hardware — either on-premise at each location, on GPU-equipped edge devices, for the lowest possible latency; or centrally in the cloud, fed by an on-site VMS (Video Management System) that streams camera feeds up for customers who would rather not invest in edge GPU hardware at every location. Which model fits is a deployment decision made per customer, not a fixed architecture.
Purpose-Built Detection Models
Custom-trained object detection models built per use case — attribute extraction, PPE verification, OCR — rather than one generic model stretched across every problem. Each of the 75+ production algorithms in the library is scoped to a specific operational signal.
Multi-Object Tracking & Identity Continuity
Object tracking maintains consistent identity across frames — the foundation for anything that depends on duration or journey, from queue dwell time to end-to-end drive-thru vehicle tracking.
Zero-Touch Pipeline Design
Where the physical workflow allows it, inspection triggers automatically — a component entering a defined bounding box on a conveyor line starts the pipeline with no manual capture step, as in the SPDR continuous PDI pipeline.
Metadata-First Architecture
Only structured JSON metadata — bounding boxes, OCR text, classifications, timestamps — leaves the edge device by default. Raw video stays local unless specifically retrieved for an evidence clip, keeping bandwidth and data exposure minimal.
AI in Action
Applied Engineering Deep-Dives
Three production deployments, documented at the level of what the model actually detects and how the pipeline was built.
The Pizza Analyzer: Quality Grading at Prep Speed
A computer vision classifier that inspects topping distribution, crust geometry, and bake level in the seconds between assembly and boxing — fast enough to fit inside an existing prep workflow rather than adding a step to it.
SPDR: A Zero-Touch Continuous Inspection Pipeline
Finished components travel vertically on an overhead chain conveyor; the GPU pipeline triggers automatically as each part enters a defined inspection bounding box — no manual capture, no operator involvement.
EEPD: Continuous PPE and Machine Utilization Without Manual Logging
Around heavy bore-machining equipment, a 6-point PPE classifier and a machine-state classifier run continuously in the background — not as a periodic safety walk, but as an unbroken monitoring signal.
Why This Matters
Every Capability On This Site Traces Back to a Production-Deployed Model
Vendor claims about AI are cheap to make and expensive to verify. This section exists so they don't have to be taken on faith: the algorithm library names the specific capabilities in production, the methodology page names the specific technologies they run on, and the spotlights document specific pipelines end to end. None of it is a lab demo or a slide-only concept — it's the same engineering layer described, in less depth, throughout the Solutions, Industries, and Products sections of this site.
Looking for the deployment infrastructure this runs on instead — edge devices, DeepStream, the cloud platform? See Technology & Architecture.
TALK TO THE ENGINEERING TEAM
Have a specific question about model accuracy, training data, or how a capability was built? Talk directly to the people who built it, not a sales layer.