Look, if you're buried in repeat rework, late defect catches, and maintenance backlogs that won't budge, you're not alone—I've watched it tank too many ops. Ombrulla's AI and IoT solutions hook straight into your inspection, reliability, and safety tools, slamming issues from 'spotted' to 'sorted' in record time.
This page helps you find the right industrial AI solutions for your plant or site. It is a hub, so each section is short and links to the full solution page.
If you are dealing with recurring defects, unplanned downtime, slow work-order closure, or safety incidents that turn into paperwork, start here.
You will see our AI and IoT solutions and where each one fits in real operations. If you want, we can start with a small pilot and measure the before/after.
Most AI pilots spot problems, but nothing changes on the floor. Alerts show up, teams stay busy, and issues don’t get closed. Ombrulla builds industrial AI solutions that push work forward, not just insight.
We start with the workflow. Our AI and IoT solutions connect signals to action:
You should see changes in numbers you already track: defects, downtime, closure time, and safety response. Results depend on your baseline, so we focus on what can be measured and repeated.
You catch issues earlier and route them to the right owner with context. Less firefighting, fewer repeat breakdowns, and fewer work orders stuck in “open” for weeks
Inspection stays consistent across shifts. When something is flagged, the evidence is saved and the next step is clear, so defects do not travel downstream unnoticed.
Alerts are useful only if they trigger action. We help teams respond faster and keep a clean timeline of what happened, when it happened, and what was done.
You get one way to capture evidence and close the loop, even if each site runs a little differently. That makes audits simpler and rollouts easier.
Two platforms sit under most of what we deliver.
Tritva handles visual inspection where decisions have to be fast and consistent. PETRAN is the backbone that connects edge signals to cloud context and routes outcomes into day to day work.
Use Tritva when quality checks cannot depend on who is on shift, or when defects need to be caught before they travel downstream.
Predictive signals let you focus resources on the assets most likely to fail soon and most costly to stop.
Most failures happen after the demo, when real shifts, real data, and real approvals show up. This is how we keep the work moving from “looks good” to “runs every day”.
Computer vision for defect detection and quality control built for speed, consistency, and real time decisioning.
Drone + rover + mobile inspection workflows, with strong claims around cost reduction, cycle time improvements, and shifting work from reactive to predictive.
Built to fit the workflow and the systems behind it (ERP/MES/CRM/internal tools), with pilots designed to prove ROI before scale.
Autonomous agents that can reason, call systems, run multi step workflows, and produce searchable audit trails (beyond chatbots/RPA).
A unified APM model fusing IoT sensing, edge intelligence, and cloud AI to predict failures and raise OEE.
Condition monitoring + RUL forecasting + CMMS/EAM integration to reduce downtime and improve planning.
Live view of assets, utilities, and operations with clear “monitoring vs APM” framing and measurable outcome ranges.
A live virtual model combining assets, people movement (RTLS), processes, environment, and energy used for visibility and optimisation.
Field capture → AI analysis → routed actions, with evidence that holds up during audits. Includes gauge/indicator reading and structured checklists.
Turns day to day operational decisions into measurable sustainability outcomes (energy, emissions, water, waste) without sacrificing throughput or quality.
RTLS + sensors + workflows for safety alerts, musters, and compliance evidence built for high risk environments.
Real time location, check ins, SOS/duress, man down detection, and industry specific workflows (oil & gas, utilities, maritime, construction).
Privacy first occupancy and footfall analytics with edge processing options and multi camera coverage.
On a plant site, the first question is usually not “is the model accurate?” It’s “who can touch this, and what happens if it’s wrong?”
We keep access role-based. And we keep a record. If a defect is flagged or a safety alert is raised, you can see when it happened, who saw it, what action was taken, and who approved it.
We agree this early. Some teams want to keep raw video for a short window. Some don’t want it stored at all. We set retention rules, lock down access, and encrypt what’s stored and what’s moving.
If a line needs instant decisions, you do more at the edge. If you want cross-site reporting and learning, you use cloud. Most teams end up hybrid, because plants are not all the same.
Connect to historians, PLC/DCS systems, IoT platforms, and data lakes through open APIs, without forcing a single vendor stack.
Turn risk scored insights into guided work orders and feed technician outcomes back into continuous improvement.
Version tracking, drift monitoring, safe rollback, RBAC/SSO, audit trails, and encryption, deployable in cloud, on prem, or private environments.
Fast triage at the edge, deeper learning and fleet context in the cloud so you get speed and scale.
They help reduce defects, unplanned downtime, safety risks, and wasted energy by turning camera and sensor signals into actions. Instead of showing data in dashboards, they trigger alerts, work orders, inspection results, and escalations, so teams respond faster and results repeat across shifts and sites.
AI visual inspection and condition monitoring are usually fastest because the impact is easy to measure. You can track defect rate, scrap, rework, downtime minutes, and response time. The best pilots stay narrow, run in real production, and show a clear before and after.
Most real pilots take about 4 to 8 weeks. The timing depends on data access, approvals, and how much integration is needed. Demos can be quicker, but they do not prove reliability. A good pilot includes validation, workflow testing, and sign-off from frontline teams.
Not always. Many projects start with existing cameras, SCADA or PLC signals, historian data, and maintenance records. You add new sensors only if current signals cannot answer the operational question. The goal is to prove value first, then invest deeper where it pays back.
You need real images or video from normal operations, including different lighting, angles, surface variation, good parts, bad parts, and tricky edge cases. Training only on perfect samples usually fails on the line. The dataset should reflect how defects actually appear in production.
Accuracy depends on defect type, camera setup, and process stability. More important is the tradeoff between false rejects and false accepts. Teams set thresholds based on cost of scrap, rework, and escape risk, then keep validating as conditions change on the line.
Predictive maintenance focuses on forecasting failures for specific equipment so maintenance can be scheduled earlier. APM is broader. It combines monitoring, risk scoring, and context to help prioritise what to fix first and route actions into maintenance workflows, not just report anomalies.
You limit alerts to what someone can act on. That means scoring severity, adding context rules, and defining ownership and escalation paths. Alerts should feed workflows, not spam notifications. A healthy system reduces noise over time. If alert volume keeps rising, it needs tuning.
Yes, and they should. Without integration, insights stay in dashboards and teams stop using them. The practical goal is routing outcomes into daily tools: work orders in CMMS or EAM, quality records in MES, incident logs in EHS, and prompts for operators.
Hybrid is most common. Edge processing helps when you need low latency, privacy, or stable operation during network issues, especially for vision and safety. Cloud helps with fleet learning, central management, and analytics at scale. The right choice depends on site constraints.
Assume drift will happen. Lighting changes, suppliers change, equipment wears, and processes evolve. You handle it by monitoring performance, sampling new data, retraining on a schedule or trigger, and keeping version control. That way changes are auditable and rollbacks are possible.
Ask who can access raw data, how permissions are controlled, where audit logs are stored, and how investigations are supported. Also ask who can approve automated actions and what safeguards exist. Good governance is what makes automation safe to scale across sites and teams.
They combine location context with event triggers like SOS, man-down, geofence breaches, and no-movement. When something happens, they escalate to the right people with a clear timeline. The point is faster intervention and defensible evidence during reviews, especially for remote, high-risk work.
A digital twin is a live model of operations that brings together asset data, environment readings, movement, and energy signals in one place. It is useful when it supports real decisions, like planning maintenance, testing scenarios, or improving throughput versus energy, not just visualisation.
A chatbot answers questions. Agentic AI can execute work steps: collect context, apply rules, call systems, draft actions, and log what it did. The key is control. You need boundaries, approvals, and traceability so automation speeds execution without creating hidden operational risk.
