How we validate
The methodology behind every detection claim
Before you deploy AI at a guarding site, you need to understand exactly how outcomes were produced. Our four-layer validation process is designed to withstand scrutiny from security operations directors, IT procurement, and compliance teams simultaneously.
Integration & compliance standards
Standards your security
procurement team will recognise
These are not marketing claims. Each represents a confirmed technical integration decision.
Six scenarios. One platform.
Each scenario is independently configurable and can be combined for comprehensive site coverage.
Authorized vs. unauthorized vehicle classification
VizAIo processes video from remote guarding sites in MP4 and AVI format, identifying vehicles by type, license plate, color, make, and model — then cross-referencing against a customer-supplied authorized vehicle database. Unauthorized vehicles are flagged with annotated frame evidence, timestamps, and classification reason. Background checks against stolen-vehicle APIs run automatically.
- Guards manually scan footage for unfamiliar plates
- No structured log of vehicle ingress/egress events
- Stolen-plate checks require manual lookups
- Multi-vehicle frames missed during busy periods
- Automatic classification of every vehicle in every frame
- Real-time Immix alarm on first unauthorized detection
- Stolen-vehicle and criminal-activity API background checks
- Structured metadata: plate, type, timestamp, location
PPE compliance monitoring across construction and dock sites
VizAIo detects workers missing hard hats, safety vests, boots, and other required PPE across active industrial zones and riverside dock environments. The system provides worker-level detail: clothing color, hat status, position in frame, and movement direction — enabling safety supervisors to act on violations immediately rather than discovering them in post-shift audits.
- Periodic supervisor walkthroughs miss violations
- No continuous coverage on 24-hour construction sites
- Incident reports reactive, not preventive
- Dock workers near water edge unchecked for life vests
- Continuous 24/7 PPE audit — day and night cycles
- Per-worker annotation with equipment status
- Real-time alert when life vest absent near water edge
- Audit log with frame evidence for EHS compliance records
Loitering, crowd detection, and perimeter intrusion
VizAIo applies polygon geofencing to define protected zones — material storage areas, server rooms, aquaculture farm perimeters, building entry points — and triggers real-time alerts the moment an unauthorized person or animal is detected. Loitering duration thresholds are configurable, and alerts are classified by zone and behavior type before being routed to Immix.
- Guards monitor static feeds — attention fatigue causes missed events
- No distinction between authorized movement and intrusion
- After-hours violations discovered only in next-day review
- No alert on package or unknown person at entry
- Polygon geofences fire on first unauthorized entry
- After-hours office movement triggers immediate security alert
- Aquaculture: poacher and predator detection on thermal feeds
- Entry-point crowd and package detection with Immix routing
Running, falling, and aggressive behavior detection
Using pose estimation and spatio-temporal action recognition, VizAIo detects risk scenarios — people running, slipping, falling, or displaying aggressive behavior — in real time. This applies across construction sites, dock loading zones, office common areas, and building entry points. Detection triggers immediate alert routing to the appropriate response protocol.
- Falls discovered after injury — no proactive alert
- Running near hazardous machinery undetected
- Aggressive behavior at entry points escalates before response
- Slip events on docks reach water before help arrives
- Pose estimation detects fall posture in under one second
- Running detection triggers zone-specific risk alert
- Entry point aggression routed to security response team
- Combined with thermal for dock perimeter risk coverage
24/7 perimeter monitoring on thermal cameras
VizAIo processes thermal camera feeds for person and vehicle detection in low-light and outdoor conditions where RGB cameras fail. Purpose-built for aquaculture farm perimeters, riverside boundaries, and large outdoor industrial zones. Detects poachers, predators, and unauthorized vehicles using thermal signature classification, with configurable alert thresholds per zone.
- Night-time intrusions undetectable on standard CCTV
- Thermal footage requires manual review — no automation
- Aquaculture poaching happens between guard patrols
- No object classification — thermal blobs unidentified
- 24/7 automated thermal detection — day and night parity
- Person vs. vehicle vs. animal classification on thermal feeds
- Configurable intrusion boundary with immediate alert
- Integrated with PPE and risk detection in one pipeline
Natural language search across recorded video archives
VizAIo's forensic query engine lets investigators describe what they're looking for in plain language — "find the moment someone breaks the glass door" or "show all frames where a blue car is parked near the entrance" — and returns exact timestamps, annotated frames, and event summaries without manual footage scrubbing. Incidents are surfaced in seconds, not hours.
- Investigators scrub hours of footage manually per incident
- No way to search by described behavior or object
- Key frames buried in multi-gigabyte archives
- Evidence export requires full clip extraction
- Natural language query returns timestamped frames instantly
- Annotated evidence frame included in every AI response
- VizAIo compresses footage to notable-only frames for review
- GrasPh links evidence across multiple documents and feeds
Encounter Records to Early Clinical Signals
Converts structured encounter data into clinically meaningful insight — identifying disease progression patterns, delayed diagnosis risks, geographic access effects, and emerging capacity pressures using existing clinical and administrative data sources.
- EHR data optimised for billing, not intelligence
- Fragmented longitudinal patient record visibility
- Unquantified access-to-care disparities
- Retrospective utilisation-based demand planning
- FHIR Encounter / Condition / Procedure sequence mining
- Risk-adjusted benchmarking across multi-EHR environments
- Geospatial access inequity quantification
- ADT + FHIR streams → predictive demand signals
Visual Observation to Measured Patient Flow
A privacy-preserving computer vision system that quantifies patient flow, room utilisation, and congestion dynamics using existing camera infrastructure — with zero PHI exposure.
- Unmeasured throughput latency across flow stages
- Manual room and resource status tracking
- Heuristic staffing vs encounter-driven demand
- HIPAA / PHI exposure risk with camera systems
- FHIR-driven room & chair visibility
- HL7 v2 ADT-informed staffing optimisation
- HIPAA-compliant edge analytics — no PHI stored
- Sub-second zone state updates (<1s latency)
Contract Interpretation to Structured Compliance Validation
Standardises contract validation across heterogeneous healthcare documents — from 837 claims artifacts to payer contracts — eliminating reviewer-dependent interpretation and missed requirement exposure.
- Clause-by-clause manual validation overhead
- Missing requirement exposure risks
- Reviewer-dependent interpretations
- No structural visibility across document sets
- 837 claims, HL7 messaging, ICD-10, SNOMED CT aware
- Automated structural parsing & semantic indexing
- Reference-standard deviation detection
- Weighted compliance scoring + audit-ready reports
Equipment Specs to Deterministic Performance Validation
Interprets equipment manuals, safety constraints, and operating specifications — delivering traceable, explainable validation for biomedical and clinical engineering teams who previously depended on SME availability.
- Fragmented device / equipment data sources
- Unvalidated safety & constraint rules
- SME-dependent operational analysis
- No explainable decision reasoning
- HL7, FHIR, device manuals, SOPs ingested natively
- Deterministic + AI reasoning for constraint-aware analysis
- Automated OEM / accreditation compliance validation
- Cross-device benchmarking — imaging to therapeutic assets
Competitive positioning
How Prajna AI compares to traditional
video analytics approaches
Differences that matter to security operations directors and IT procurement teams.
| Dimension | Traditional Rule-Based VMS | Prajna AI — VizAIo |
|---|---|---|
| Incident identification | Slow Manual footage scrubbing — hours per incident |
23× faster Natural language query returns timestamped frames in seconds |
| Vehicle classification | No detail Motion triggers only — no object type or plate data |
Full metadata Type, plate, color, make — cross-referenced against authorised vehicle DB |
| PPE compliance | Hardware-bound Requires dedicated hardware per zone; no per-worker detail |
Software-only Software-layer detection on existing CCTV — worker-level annotation with equipment status |
| Alert evidence | No evidence Timestamp and zone label only — no visual evidence per alert |
Frame-verified Annotated frame returned with every alert — defensible audit trail from day one |
| Privacy model | Video stored Full video retention — liability risk and storage cost |
Zero retention Edge processing — no video stored, Gaussian blur on faces, anonymous track-IDs only |
| VMS integration | Weeks to deploy Proprietary integration — weeks of custom dev per platform |
Immix-ready Immix-native alarm automation — plug-in event routing for Vehicle Detected, Stolen Plate, Anonymous Vehicle |
| Night / thermal coverage | Siloed Separate thermal system — no shared intelligence pipeline |
Unified Unified pipeline — thermal, RGB, and RTSP feeds processed through a single platform |
Agentic framework
Five layers. All auditable.
Each layer of the Prajna AI Agentic Framework is independently configurable and maps to a defined security and compliance perimeter.
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