NeoBramPlan an AI project
    Industrial AI solution

    AI Safety Monitoring For Industrial Sites

    Real-time computer vision for PPE compliance, hazard-zone intrusion, fire and smoke detection, spill detection and ergonomic risk across plants, refineries and warehouses.

    Plan the use case Explore ROI tools
    AI computer vision safety monitoring on an industrial site

    Acceptance before scale

    Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

    Direct answer

    NeoBram deploys real-time AI safety monitoring on industrial sites - PPE compliance, hazard-zone intrusion, fire and smoke, spill detection, forklift and pedestrian safety, and ergonomic risk - running on edge GPUs against your existing CCTV cameras with no privacy-impacting cloud upload.

    Private deployment available

    Why evaluate it

    CCTV Records Incidents. AI Prevents Them.

    The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

    Most industrial sites already have hundreds of cameras. They record incidents for forensic review but do nothing to prevent them. Safety officers can't watch every feed in real time.

    AI safety monitoring turns existing CCTV into a real-time prevention system. PPE violations, hazard-zone intrusions, fire / smoke / spills and unsafe forklift behaviour are detected in seconds and alerted to supervisors with the camera view, location and time.

    We deploy on edge GPUs (NVIDIA Jetson, T4) inside your network. No video data leaves the site. Detections are face-blurred by default - the system focuses on behaviour and conditions, not identifying individuals - keeping you compliant with works-council and privacy requirements.

    Candidate capabilities

    What a production solution may need to do.

    Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

    PPE compliance monitoring

    Detect helmet, hi-vis, safety glasses, gloves, hearing protection and harness compliance in real time at gates, work zones and high-risk areas.

    • Helmet, hi-vis, glasses, gloves, harness detection
    • Zone-specific PPE rules (e.g. harness in fall-zone)
    • Repeat-offender pattern detection (anonymised)
    • Compliance % dashboards by area, shift, contractor

    Hazard-zone & vehicle safety

    Detect pedestrian intrusion into forklift zones, exclusion zones around machinery, and unsafe forklift-pedestrian proximity - with sub-second alerts.

    • Geofenced exclusion-zone intrusion alerts
    • Forklift-pedestrian proximity warnings
    • Tailgating and unauthorised access at gates
    • Lone-worker detection in hazardous areas

    Fire, smoke & spill detection

    Early detection of flame, smoke plumes, liquid spills and gas-cloud visual signatures - augmenting traditional fire & gas systems with faster optical detection.

    • Flame and smoke detection (sub-30 second)
    • Liquid spill and pool detection
    • Steam vs smoke discrimination
    • Integration with site fire & gas system

    Alerting & EHS integration

    Real-time alerts to supervisors via Teams / Slack / WhatsApp with camera snapshot, and structured incidents pushed to SAP EHS, Enablon or ServiceNow for investigation and trending.

    • Real-time supervisor push with snapshot
    • Structured incident creation in SAP EHS / Enablon
    • Safety KPI dashboards with leading indicators
    • Root-cause trending across sites

    Architecture context

    Select components after the boundary and test are clear.

    The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

    Cameras & infrastructure

    • Existing IP CCTV (Axis, Hikvision, Hanwha)

      Reuse current camera estate

    • ONVIF / RTSP

      Standard video ingestion

    • Milestone XProtect / Genetec

      VMS integration

    Detection models

    • YOLO v8 / v11

      Real-time object & PPE detection

    • Custom CNNs

      Fire, smoke, spill classifiers

    • Pose estimation (MediaPipe, OpenPose)

      Ergonomic risk

    • ByteTrack / DeepSORT

      Multi-object tracking

    Edge inference

    • NVIDIA Jetson Orin / IGX

      Edge GPU per cluster

    • DeepStream SDK

      Multi-camera GPU pipelines

    • TensorRT

      Optimised inference

    Alerting & workflow

    • MS Teams / Slack / WhatsApp

      Supervisor alerts

    • ServiceNow / SAP EHS

      Incident ticketing

    • Power BI / Grafana

      Safety KPI dashboards

    Planning ranges

    A standard path from decision to operation.

    01

    Discovery and qualification

    1-2 weeks

    Named workflow, owner, baseline, risks and go/no-go questions.

    02

    Readiness assessment

    2-4 weeks

    Data, integration, security, value and operating-readiness findings.

    03

    Technical proof of value

    4-6 weeks

    A bounded test on representative data with documented limitations.

    04

    Production pilot

    8-12 weeks

    One controlled workflow, integrated and evaluated with real users.

    05

    Enterprise or multi-site rollout

    3-6+ months

    Phased scale-out, monitoring, support and change management.

    06

    AI capability or CoE programme

    3-6+ months

    Governance, delivery methods, reusable assets and team enablement.

    These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

    Production safeguards

    Private deployment is one control, not the whole control system.

    Data boundary

    • Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
    • Document data flows, storage, deletion, backups and support access before production.
    • The customer approves every interface and any permitted external connection.

    Model and application controls

    • Evaluate representative cases, uncertainty and harmful failure modes before use.
    • Use suitable access control, input handling and output guardrails for the selected risk.
    • Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

    Traceability and operation

    • Define identity, roles, logs, monitoring, updates, backup and incident handling.
    • Keep the evidence needed to investigate outputs and reproduce important decisions.
    • Assign a named business and technical owner for production operation.

    Human authority

    • Domain, quality, safety and regulatory owners retain decision authority.
    • Compliance depends on the implemented system and the customer's validated controls.
    • Escalation and safe fallback are part of the acceptance criteria.

    Limitations to plan for

    Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

    Buyer questions

    Direct answers before you plan a pilot.

    How do you handle worker privacy and works-council concerns?+
    Privacy is the default. The system detects behaviour and conditions (PPE on / off, person in zone, fire present) - not identities. Faces are blurred in stored snapshots. We don't run facial recognition. Detections are anonymised. We work with your works council or union to agree the deployment scope, retention period and access controls before go-live, and we provide a documented data-protection impact assessment (DPIA).
    Can we reuse our existing CCTV cameras?+
    Yes - in most deployments we use your existing IP cameras via ONVIF or RTSP. Cameras that are too low resolution, poorly positioned or have heavy occlusion may need to be upgraded or relocated for high-confidence detection in critical zones. We do a camera audit during discovery.
    Where does video processing happen?+
    On edge GPUs inside your network - typically NVIDIA Jetson Orin or T4 / A10 servers in your IT room. Video never leaves your site. Only structured detection events (with optional snapshot) are sent to alerting and dashboards. This addresses both privacy concerns and bandwidth limits at remote sites.
    How does this integrate with our existing VMS (Milestone, Genetec)?+
    We sit alongside your VMS, consuming the same RTSP streams. Detections can be pushed back into the VMS as bookmarks / events so security operators see them in their normal workflow. The VMS remains your system of record for video; we add the AI detection layer.
    What about false alarms and alarm fatigue?+
    We tune detection confidence thresholds per zone and per detection type during the pilot, with a target of <2 false alerts per camera per shift. Alerts are grouped (one event per intrusion, not one per frame). Supervisor feedback in the UI labels false positives, which feed scheduled model retraining to reduce them further.
    What's typical pilot scope and timeline?+
    A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.

    Bring one workflow

    Define the evidence, boundary and acceptance test together.

    Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

    Plan the first project