Replace subjective manual checks with objective, AI-driven evidence. Ombrulla's field inspection platform, built on PETRAN, enables inspectors to capture photos and videos on any mobile device and receive instant AI analysis, automated alerts, and fully auditable findings. No connectivity required in the field.
Mobile AI inspection uses smartphone or tablet-based computer vision to evaluate the condition of industrial assets directly in the field. By turning photos, videos, and structured visual evidence into instant, AI-driven insights, it replaces slow manual inspections with faster, more consistent, and auditable decision-making.
- Detect issues early: AI analyses field images against reference standards to identify corrosion, damage, missing parts, misalignment, and other anomalies.
- Automate action: The platform can read gauges with OCR, score findings by severity and confidence, and automatically trigger alerts or route work orders when thresholds are met.
- Strengthen compliance: Every inspection is logged in a timestamped, tamper-evident audit trail, giving asset-intensive industries stronger documentation for safety and regulatory review.
AI converts raw field visuals into prioritised findings within seconds of sync. Teams receive actionable work orders — with evidence, location, and severity — before the inspector leaves site. Organisations report up to 65% reduction in inspection-to-action cycle time, directly lowering MTTR and unplanned downtime costs.
Human inspection is inherently variable. AI applies identical detection criteria uniformly across every site, shift, and geography — eliminating quality drift caused by inspector fatigue, experience disparities, and subjective judgement. Consistency is no longer a function of individual performance.
Fewer repeat visits, shorter cycles, and automated data entry eliminate hidden costs. When defects are caught earlier and work orders generated automatically, costly emergency repairs become planned maintenance events. Typical reduction: 30–45% in cost-per-inspection within year one of deployment.
Every finding carries a complete provenance record: original image, AI verdict and confidence score, triggered rule, capturing inspector, and approving supervisor. This chain of evidence meets ISO 55000, API 580, and sector-specific regulatory frameworks — with zero additional manual effort.
Configure asset-specific inspection standards in PETRAN — including pass/fail criteria, mandatory evidence types, SLA rules, and visual reference images that establish the accepted baseline for each asset. Checklists sync automatically to mobile apps. When the AI compares a field image to its reference, it has a precise, asset-specific baseline — not a generic model trained on industry averages.
The mobile app supports single-shot photography, short video clips, and burst-mode capture for moving targets or rapidly degrading conditions. AI processes individual frames and video sequences to maximise detection reliability. On-screen prompts, angle overlays, and mandatory shot indicators enforce evidence completeness before the inspector moves to the next checklist item.
The computer vision engine performs frame-by-frame comparison of captured images against asset-specific reference baselines — detecting missing components, physical damage, corrosion, surface degradation, and misalignment. Each finding is assigned a severity score and detection confidence level, enabling prioritised triage and appropriate escalation automatically.
Analog dials, digital readouts, nameplates, and identification plates are automatically read using OCR. Extracted values pass through a configurable rule engine: if a pressure reading exceeds 85% of maximum rated pressure, the system instantly creates an urgent work order, notifies the site supervisor, and sets a 4-hour SLA — without any manual intervention. Supports stacked logic: gauge value + asset criticality + days since last service.
Designed for industrial reality — not ideal network conditions. The app caches checklists, reference images, and captured evidence locally with no dependency on connectivity. Inspectors work at full capability in remote oil and gas sites, ship engine rooms, underground utility tunnels, or any low-connectivity environment. When connectivity is restored, a conflict-safe sync engine uploads all data and resolves simultaneous edits automatically.
AI findings that trigger configured rules automatically generate structured actions: work orders in PETRAN or connected CMMS/EAM systems including SAP PM and IBM Maximo, push notifications and email alerts to responsible teams, and SLA-bound due dates. All context — photos, readings, location, and severity — travels with the work order, eliminating information loss in the field-to-workshop handover.
Role-based access control, AES-256 encryption at rest and in transit, full audit logging, and configurable data residency options meet the security and compliance requirements of enterprise IT and regulated industry environments. Deployment options: cloud-hosted SaaS, private cloud, and on-premises. Every AI decision, rule execution, and user action is logged and tamper-evident.
Operational dashboards surface defect trends by asset type, site, and time period — alongside MTTR and MTBF impact analysis and false-positive/negative tracking. Inspector overrides and annotated corrections feed into model retraining cycles automatically, improving detection accuracy with every inspection run. Scheduled executive reports keep leadership informed without manual data assembly.
Standardize field inspections with guided checklists and AI-assisted defect detection.
Outcome
Primary Use Cases: Pipeline, tank & rotating equipment inspection.
Key AI Detections: Corrosion, coating failure, gauge anomalies, leak indicators.
Primary Use Cases: Progress verification, quality assurance, HSE audits.
Key AI Detections: Cracks, spalling, rebar exposure, milestone deviation.
Primary Use Cases: Hull, engine room & safety apparatus inspection.
Key AI Detections: Fouling, coating degradation, dial anomalies, lashing faults.
Primary Use Cases: Grid asset, substation & meter inspection.
Key AI Detections: Insulator cracks, tamper cues, vegetation encroachment.
Primary Use Cases: HVAC, fire safety & structural portfolio checks.
Key AI Detections: Water ingress, panel hotspots, fire equipment status.
Primary Use Cases: Solar PV, wind blade & substation inspection.
Key AI Detections: Soiling, micro-crack indicators, blade anomalies, oil leaks.
Because checklists, reference images, rules, and actions are managed within PETRAN, there’s no separate inspection system to maintain. AI mobile inspection extends the asset platform teams already use—reducing training time, accelerating time-to-value, and avoiding integration complexity.
Laboratory-trained models often fail in real-world conditions. Ombrulla’s computer vision is trained on industrial datasets, handling variable lighting, angles, occlusion, weathering, and complex asset conditions. The result is reliable performance aligned with real inspection environments across sites and shifts.
The rule engine goes beyond simple alerts, combining conditions like gauge readings, asset criticality, and service history to route findings with the right priority. Configured by operations teams via a no-code interface, rules can be updated instantly—ensuring business outcomes drive the system, not technical constraints.
Connect to CMMS, EAM, or ERP systems via SAP PM, IBM Maximo, or REST APIs. Maintain full control with flexible data residency across cloud, private cloud, or on-prem deployments. Every AI decision, rule, and user action is immutably logged—ensuring a defensible audit trail for compliance and regulatory needs.
| Dimension | Traditional Manual Inspection | Ombrulla AI Mobile Inspection |
|---|---|---|
| Consistency | Varies by inspector, fatigue level, and field experience | Identical AI criteria applied on every inspection, every site, every shift |
| Finding-to-Action Speed | Hours to days via manual report writing, triage, and routing | Seconds to minutes via automated rule-based routing and work order creation |
| Documentation Quality | Dependent on individual inspector diligence and discipline | Complete, timestamped, tamper-evident chain of custody from capture to closure |
| Offline Capability | Paper forms — high risk of data loss on manual transfer | Full offline capture with conflict-safe automatic sync when connectivity restored |
| Gauge Reading Accuracy | Subject to parallax error and manual transcription mistakes | OCR-extracted values, threshold-triggered alerts, fully automated |
| Audit Trail | Dependent on filing discipline and individual record-keeping | Built-in, searchable, from capture through AI verdict to closed work order |
| Cost Per Inspection | High — repeat visits, rework, administration overhead | Up to 45% lower within the first year of deployment |
| MTTR Impact | Delays between detection, reporting, and remediation action | Up to 65% reduction in inspection-to-action cycle time |
| Regulatory Defensibility | Risk of gaps under ISO 55000, API 580 scrutiny | Meets ISO 55000, API 580, IEC, NERC CIP — by design, not manual effort |