Industrial AI & IoT Solutions for Operations, Quality, and Safety

Industrial AI and IoT help businesses detect quality defects, monitor equipment health, identify safety risks, and improve operational visibility. Ombrulla combines TRITVA for AI visual inspection with PETRAN for asset performance management and predictive maintenance, connecting findings to alerts, maintenance tasks, and operational workflows.

Start with a clearly defined problem that has measurable business impact and accessible data. Suitable examples include a recurring visual inspection task, condition monitoring for a critical machine, or safety monitoring in a designated zone. Ombrulla helps assess feasibility, establish a baseline, and agree on pilot success criteria.

A focused Ombrulla industrial AI pilot typically targets 4–8 weeks, depending on site access, hardware availability, data readiness, and integration scope. The pilot covers setup, configuration, testing, and results review. Predictive maintenance applications may require longer observation periods to validate failure prediction or remaining useful life estimates.

Existing equipment can be reused when it meets the selected application's requirements. Ombrulla assesses camera resolution, lighting, viewing angles, sensor coverage, connectivity, and data access before recommending hardware. Additional cameras, sensors, or edge devices may be needed where existing equipment cannot capture the required inspection or monitoring data.

AI visual inspection needs clear images showing acceptable products and the defects or conditions the system must identify. Requirements depend on defect size, product variation, lighting, and camera positioning. For custom inspections, Ombrulla defines image collection and labelling requirements during discovery and validates the model using representative production samples.

AI visual inspection performance should be measured using defect detection rate, missed defects, false rejections, and inspection speed. TRITVA is evaluated against agreed acceptance criteria using representative production conditions. Results depend on image quality, defect visibility, product variation, and training data, so performance is validated for each application.

Predictive maintenance uses equipment data to identify deterioration and estimate when maintenance may be needed. Asset performance management (APM) is broader, combining asset health, condition monitoring, reliability analysis, and maintenance decision support. PETRAN brings these capabilities together to help teams prioritise interventions and improve equipment availability.

IoT monitoring reduces unnecessary alerts by applying contextual thresholds, persistence rules, severity levels, and clear response ownership. PETRAN can be configured to prioritise relevant events and route notifications to responsible teams. Linking alerts to tracked maintenance or operational tasks helps teams manage issues through to resolution.

Yes. Ombrulla supports integration with enterprise and industrial systems through APIs, connectors, and supported communication protocols. Integration scope can include reading equipment data, creating maintenance work orders, updating quality records, and synchronising asset information. Compatibility, permissions, and required customisation are assessed for the systems used at each site.

Edge deployment processes data near equipment for fast local responses. Cloud deployment supports centralised analytics and access across sites. On-premises deployment keeps processing within customer-managed infrastructure. A hybrid approach combines local processing with central reporting. Ombrulla recommends an architecture based on connectivity, latency, data requirements, and operational scale.

AI models need review when products, materials, lighting, equipment, or operating conditions change. Performance monitoring and verified user feedback help identify when an update is needed. Updated models should be tested against agreed acceptance criteria and approved before production deployment, with version control supporting traceability and rollback.

Agree on data storage, ownership, retention, user access, encryption, system connectivity, and audit requirements before deployment. Also define who approves model updates and which AI-triggered actions require human authorisation. Ombrulla's deployment scope should document the applicable controls, responsibilities, and customer IT requirements.

AI workplace safety monitoring analyses camera feeds to identify configured events such as missing PPE or entry into restricted zones. Lone worker monitoring uses connected devices or mobile applications for location updates, SOS alerts, and scheduled check-ins. Available features depend on the selected hardware, connectivity, and site response procedures.

An industrial digital twin is a digital representation of a physical asset, system, or facility that is updated using operational data. It can combine equipment status, sensor readings, inspection findings, and maintenance history. Depending on its configuration, it supports condition assessment, planning, and analysis of operational changes.

Agentic AI uses software agents to interpret information and carry out tasks within defined permissions. Industrial applications can include evaluating an equipment alert, retrieving maintenance context, recommending a response, and creating an approved work order. Chatbots primarily support conversations, while conventional robotic process automation typically follows predefined task sequences.

Pilot value is measured by comparing results with an agreed operational baseline. Relevant measures include missed defects, false rejections, inspection time, equipment downtime, maintenance effort, and response time. Ombrulla uses the pilot review to assess technical performance and potential business value before recommending a wider rollout.