Cogni Vista: vision that anticipates, not just detects.
Turn passive surveillance into proactive, intent-aware visual cognition. Multimodal fusion, neural attention and context-aware reasoning run on the edge, so hazards are flagged seconds before they happen, with every decision explained.
About these figures. They are the reported outcomes of 12-month pilots (accuracy measured on a custom industrial dataset). Treat them as reported product results; independent verification needs the underlying evaluation records.
01 · The shift
From reacting to the event to acting before it
Legacy vision AI in high-stakes environments suffered systemic limits. Stakeholders asked for proactive cognition: anticipate hazards 5–30 seconds ahead, reason across modalities, adapt online and explain every decision.
Same hazard, two systemsWhen does the alert fire?
Cogni Vista warns 5–30 s aheadHazard eventLegacy alert after the fact
Illustrative timeline.
Legacy vision AICogni Vista
Reactive only
Detects events after they occur: the PPE violation has already happened.
Anticipatory
Forecasts hazards 5–30 seconds ahead so intervention happens before the event.
High false alarms
Operator fatigue and alert desensitisation.
Intent-scored alerts
Context and intent scoring cut the false positive rate to 0.31%.
Fragile perception
Struggles with occlusion, variable lighting and rare events.
Cross-modal fusion
RGB, depth, thermal and audio cross-check each other; synthetic augmentation covers rare events.
Cloud latency
100–500 ms round trips are too slow for real-time intervention.
Edge-first inference
About 14 ms average latency at 4K / 30 FPS on edge hardware.
Privacy & bandwidth
Centralised processing pushes raw video off site.
On-device privacy
No raw data transmission required.
No intent or context
Cannot tell playful worker movement from hazardous behaviour.
Intent and memory
Short-term memory and scene history weigh what a movement actually means.
02 · Deep architecture
One pipeline, from perception to cognition
Five layers turn raw sensor streams into explained, actionable decisions. Select a layer to open it.
Layer 01
Multimodal Input Fusion
Disparate signals are aligned before reasoning begins.
Primary RGB / 4K video
Depth (LiDAR / RealSense), thermal, audio cues and sensor telemetry
Early fusion via cross-modal attention aligns the different signals
Integration with PLCs, MQTT and CAN bus for automated shutdowns and alerts
PLCMQTTCAN busshutdown · alert
03 · Observe · Think · Verify
See how it reasons about intent
Pick a scene. The system observes, thinks with scene memory, then re-checks the evidence before it acts. Every alert carries a visual rationale and a text justification.
Interactive · explainabilityIllustrative scenes, not pilot data
EDGE-07 · 4K
intent 0.91
Observe
Think
Verify
Intent score0.91
Look-ahead≈ 12 s
Action
04 · Results dashboard
Reported pilot results at a glance
Filter by pilot to see what the original case study reports. These are published claims, not independently audited results.
Reported pilot resultsCogni Vista outcomes dashboard
Risk reduction>60%operational & safety
Detection accuracy98.7%custom industrial dataset
False positives0.31%reported rate
Edge latency14 ms4K / 30 FPS
Headline outcomes from rigorous 12-month pilots across industrial and urban environments.
−89%false alarms vs rule-basedindustrial pilot · FPR 0.31%
Operator intervention time seconds, industrial pilot
Before
45 s
Cogni Vista
<10 s
From 45 seconds to under 10 seconds from alert to action.
Response latency milliseconds, linear scale
Cloud
100–500
Cogni Vista
14 ms
Measured at 4K / 30 FPS on edge hardware, with no cloud fallback needed during outages.
Edge thermal & power per node
≈ throttle
40 °C6590
69–79 °C peak, about 5 °C margin to throttling
<35 W average draw per edge node
Pilot scale reported
18edge nodes · assembly line
220+junctions & public spaces
12months of pilots
24/7industrial operation
Accuracy and reduction figures should be paired with the dataset, test period and baseline definition. The 62% near-miss figure comes from a 6-month pre / post comparison at one site.
05 · In the field
Two pilots, two very different environments
Pilot 1 · Industrial safety6-month pre / post comparison
Automotive assembly line
18edge nodes
Welding, painting and robotics zones, running 24/7.
Capabilities activated
Unsafe posture and motion forecasting, e.g. leaning into a moving conveyor
Equipment thermal + visual anomaly prediction
PPE and zone intrusion with intent scoring
Quantitative impact
Near-miss incidents ↓ 62%
Major equipment failures prevented: 3 high-value events
Operator intervention time 45 s → <10 s
False alarms ↓ 89% vs the rule-based legacy system
Edge nodes held under 35 W average draw
Pilot 2 · Smart cityReported pilot outcomes
Public safety & mobility
220+edge-integrated junctions & public spaces
Junctions and public spaces with on-site edge nodes.
Capabilities activated
Predictive jaywalking and collision risk scoring
Crowd anomaly and suspicious loitering anticipation
Traffic flow and violation forecasting
Quantitative impact
Emergency response activation time ↓ 58%
Traffic near-misses at pilot sites ↓ 64%
Intrusion / perimeter breach detection near 100% (zero misses in stress tests)
Zero cloud fallback needed during network outages
06 · Technical & ethical validation
Built to be trusted, not just fast
Every alert includes a visual rationale heatmap and a textual justification, and the ethical transparency layer logs the decision rationale.
Rare events were covered through synthetic augmentation.
69–79 °CPeak edge temperature
About 5 °C of margin before throttling.
<1%Demographic disparity
Bias audit of intent scoring across demographics.
07 · Strategic outlook
Scaling cognitive edge AI, 2023–2026
The pilots validate a shift toward cognitive edge AI: anticipating rather than reacting. Scaling plans include:
01
Defence perimeter security
Anticipating breaches along sensitive perimeters, with on-device processing and no raw-data egress.
02
Autonomous vehicle perception
Adding intent-aware, multimodal awareness to perception stacks that must react in milliseconds.
03
Critical infrastructure monitoring
Continuous, explainable monitoring of plants, grids and transport hubs.
08 · Client feedback
What teams say
Feedback from teams who worked with Cogni Vista.
★★★★★
“Getting a flag before a worker reaches the robot cell, not after, changed how our shift supervisors think about safety. The visual rationale on every alert made it easy to trust. Atul’s team was clear about what the system can and cannot do.”
AM
Arjun MalhotraPlant Safety Head · Automotive Manufacturing
★★★★★
“Running inference at the junction itself meant network outages did not stop the system, and no raw video left the site, which made our privacy review far simpler.”
MI
Meera IyerOperations Manager · Smart City Programme
★★★★★
“False alarms were the problem with our old setup; operators had stopped listening. Intent scoring brought alerts back to a level people take seriously. Atul walked our engineers through the tuning.”
KB
Karan BediEHS Lead · Heavy Industry
★★★★★
“Integration with our PLCs and MQTT broker was straightforward, and the conveyor slow-down response was predictable and logged every time.”
SV
Sunita VermaControl Systems Engineer · Industrial Automation
★★★★★
“The collision-risk scoring at crossings gave our control room something actionable instead of a wall of video. Atul and the team stayed responsive through the rollout.”
RD
Rahul DeshmukhTraffic Systems Consultant · Urban Mobility
★★★★★
“The decision log and bias audit gave our compliance team the evidence trail it needed. We still keep a human in the loop for review-level alerts.”
