Image and Video
RGB, low light, high-speed camera, stereo image, 360° and microscopic image.
COMPUTER VISION · EDGE AI · MULTIMODAL INTELLIGENCE
We interpret more than images;
we make sense of all data generated in the environment. Camera, thermal image, LiDAR, radar, sound, vibration, environmental sensors, device telemetry and corporate records are combined under a central artificial intelligence orchestrator with time, location and context relationships. The system determines not only “what it sees”, but what it is, why it is, and what controlled action is needed.
Camera, thermal imaging, LiDAR, radar, sound, vibration, environmental sensors, device telemetry, and corporate records are combined under a central AI orchestrator, correlated with time, location, and context. The system determines not only “what it sees,” but also what happened, why it happened, and what controlled action is needed.
LOCAL AND REAL-TIMEMULTIPLE DATA FUSIONCONTEXTUAL DECISIONSECURE ACTION
10NOO PERCEPTION FABRIC · LIVESENSE → FUSE → UNDERSTAND → ACT
NOT A SINGLE SENSOR ESTIMATE · VERIFICATION OF MULTIPLE EVIDENCE WITH TIME, LOCATION AND CONTEXTLOCAL FIRST · MULTIMODAL
01 / ALL POSSIBLE DATA SOURCES
We bring together the physical world and the corporate context
on the same decision plane.
The system is not limited to camera streams only. As needed, it evaluates physical sensors, device data, business systems and historical records together. Time, location, identity, quality and authorization information is preserved for each source.
Thermal and Spectral
Heat dissipation, temperature difference, infrared and hyperspectral data as needed.
LiDAR, Radar and Depth
Distance, speed, direction, volume, point cloud, obstacle and three-dimensional environmental perception.
Sound and Vibration
Machine sound, human voice, acoustic event, vibration spectrum and frequency change.
Environmental Sensors
Temperature, humidity, pressure, gas, smoke, light, air quality, current and energy.
Device and IoT Telemetry
Status, error code, log, usage, location, battery, connection, and health data.
Enterprise Systems
ERP, MES, WMS, CRM, maintenance, quality, order, shift, and authorization records.
Documentation and Historical Information
Procedure, technical documentation, maintenance history, event log, label, and human feedback.
02 / PERCEPTION AND DECISION ORCHESTRA
Each agent answers a different question;
the orchestrator combines the evidence into a single decision.
Resources can be evaluated individually; however, a single signal cannot be relied upon in a critical decision. The orchestrator combines the outputs of expert agents with time, location, confidence level, contradiction, and organizational rules in mind.
Data Acquisition and Synchronization Agent
Maps streams by source ID, timestamp, and location; flags lost packets, delays, and clock deviations.
Output: synchronized data windowQuality and Preprocessing Agent
Checks for blur, darkness, noise, sensor corruption, calibration, and missing data; separates unreliable input.
Output: clean data + quality scoreDetection and Feature Extraction Agent
Extracts object, person, motion, text, surface defect, sound event, frequency, distance, and other meaningful features.
Output: observation + location + confidenceMultiple Data Fusion and Context Agent
Combines image, signal, telemetry, and corporate record under the same event; makes conflicting evidence visible.
Output: contextual event recordAnomaly, Cause, and Prediction Agent
Evaluates deviation from the normal pattern, possible cause, direction of development, and likely outcome.
Output: anomaly + cause + probabilityDecision, Risk and Policy Agent
Tests the event by impact, urgency, legislation, authority and institutional rule; selects an automated, human-approved or halted path.
Output: controlled decision recommendationAction, Result and Learning Agent
Transmits command or task to the authorized system; verifies the actual result, reverses in case of error and records human feedback.
Output: verified result + audit trace03 / EDGE AI AND CLOSED-LOOP OPERATION
Data is processed at the source;
decision is generated without delay, without compromising privacy.
The model can run in an Edge AI box near the camera, on a mini PC, industrial computer, GPU workstation, or on-premises server. Defined core functions continue when the internet connection is interrupted; raw image output may not be required.
- Real-time local inference and low latency
- Keeping raw data local; Event and metadata sharing
- Offline or closed-loop operation option
- Signed model, rule, and device update
- Local buffer, secure synchronization, and post-connection transfer
- Hybrid architecture on-premises and private cloud for Edge, depending on needs
LOCAL EDGE INFERENCERAW DATA STAYS INSIDE
DEFINED LOCAL FUNCTIONS CAN RUN EVEN WITHOUT INTERNET CONNECTIONEDGE · ON-PREMISE · HYBRID
04 / LIVE MULTIPLE DATA SCENARIO
We don't just monitor a machine;
we read the symptoms with cause-and-effect relationships.
For example, a visual defect on the production line can be misleading on its own. The system simultaneously examines thermal rise, vibration frequency, motor current, product lot, and maintenance history; By piecing together the evidence, it produces controlled interventions.
MULTIMODAL INCIDENT REASONINGFACTORY LINE · EXAMPLE FLOW
PHYSICAL EVENTProduction line
Motor M-04 · Product Lot 248
EVIDENCE
- Image: surface deviation
- Thermal: +heat trend
- Vibration: frequency change
- Telemetry: current increase
- ERP: repetition in the same lot
- Maintenance: bearing life limit
FUSION + CAUSEPossible bearing wearQuality risk associated with visual defect
CONTROLLED DECISIONReduce speed to safe limitOpen maintenance taskIncrease lot quality controlHuman approval if high impact
RESULTDatafy actualExpected ↔ ActualError → stop / rollbackEvidence → audit trail
FEEDBACKResult; threshold, model and maintenance plan provide data for the next controlled version.REVALIDATE
NOT A DRY ALARM · PROVEN CAUSE, IMPACT AND APPLICABLE ACTIONOBSERVE · EXPLAIN · CONTROL
05 / DATA SECURITY AND PRIVACY
Seeing does not mean storing everything;
we process only the information that is needed.
In image and sensor projects, the data lifecycle is part of the design. What is collected, where it is processed, how long it is kept, who has access to it, and when it is deleted are determined together with the purpose of use.
Data Minimization
Only the necessary resources, resolution, sampling, and retention time for the business purpose are used.
Anonymization and Masking
Faces, license plates, screens, or sensitive areas can be masked locally; events can be generated instead of identification.
Role-Based Access
Separate permissions are applied for live streaming, recording, events, models, and device management.
Encryption and Device ID
Data transfer is protected by local logging, certificates, device IDs, and signed packets.
Retention and Deletion Policy
Raw image, section, feature, and event logs are retained for varying durations; data that has expired is deleted.
Audit and Event Logging
It tracks who accessed, which model made the decision, which action was taken, and what the result was.
06 / MODEL AND FIELD LIFE CYCLE
The model is not simply developed;
it is measured, maintained, and updated in the field.
Model behavior may change when light, camera angle, product, environment, sensor, or process changes. Therefore, data version, label quality, device profile, model performance, and field feedback are continuously monitored.
07 / APPLICATION AREAS
Wherever perception is needed,
a custom intelligence layer.
Each use case is designed with its own data sources, risks, regulations, human approval, and field conditions.
Quality and Production
Defect, measurement, assembly, safety, process deviation, and predictive maintenance.
Logistics and Warehousing
Packaging, palletizing, counting, damage, routing, loading, area security, and operational flow.
Retail and Smart Space
Shelf, inventory, density, queuing, space utilization, and anonymous behavior analysis.
Health and Care
Operational support, safe zone, device status, and human-certified incident management.
Educational Campuses
Privacy-focused monitoring of area security, capacity, access, device, and facility operations.
Transportation and Smart City
Flow, event, infrastructure status, environmental risk, and local Edge decisions.
Energy and Infrastructure
Thermal anomaly, site safety, equipment health, and remote facility monitoring.
Smart Product and Device
Product-embedded visual perception, voice, sensor fusion, and offline decision-making.
10NOO DIGITAL · EDGE INTELLIGENCE