A single disturbance fires dozens of correlated alarms and the operator receives more than they can process. When everything is urgent, the alarm stops informing and starts getting in the way.
02
The data exists and sits isolated
Historian on one side, CMMS on another, spreadsheets in between and OEM equipment exposing nothing. Without context, not even the best model has anything to work with.
Less than half
Share of collected industrial data that is actually used, according to a global study of more than 1,500 manufacturers: the first profitable project is usually ordering the data, not modelling it.
Source: Rockwell Automation, State of Smart Manufacturing 2025
03
AI projects with no pain behind them
A model without access to enough data, a pilot without an economic indicator, or a generic chatbot as the core product. Money is spent, little is proven, and the trust for the next project is burned.
04
Automating the process exactly as it stands
Digitising a process without auditing it first leaves the same process running faster and far more expensive to change. Some processes did not need automating: they needed deleting.
What is being quoted
The projects this sector is asking for.
This is not a list of what we can do: it is what is being tendered and quoted in this industry right now. If your problem is on it, the conversation starts with context.
Alerting and event management platform
Deduplication, intelligent suppression, grouping by event, causal correlation, dynamic severity, escalation and traceability of what was done with each notice.
Anomaly detection over time series
Especially useful when there are not enough labelled failures for supervised training: pumps, pipelines, motors, electrical systems and continuous processes.
Computer vision for industrial safety
Protective equipment use, intrusion, smoke and fire, spills, conveyor condition and counting, processed at the edge when the response has to be immediate.
OT data platform
Modbus, OPC UA, MQTT, REST, LoRaWAN, CAN and SDI-12 connectors, plus contextualisation to leave the data fit to feed analytics.
Contextual copilots
Connected to manuals, procedures, alarms, historian, CMMS and past failures. A copilot that does not know the real state of the plant adds nothing you could not already search for.
Prototypes and special projects
Custom development, assemblies and temporary structures, and unconventional solutions on demanding timelines.
How we work here
Our capability, applied to software and special projects.
01
Field
Whatever is already installed: PLCs, instrumentation, cameras and OEM equipment, plus the sensors missing to close the variable that matters.
02
Link
Industrial connectors over the network that exists, with edge processing where latency or link outages do not allow a round trip to the cloud.
03
Platform
Ingestion, contextualisation, history and an event engine: rules where the physics is known, models where it is not.
04
Decision
Every alert answers what happened, how serious it is, what the most likely cause is and what action follows. If it does not answer all four, it is a notification.
We start with a bounded pilot, build alongside your operations team and measure what matters. If we propose something, we can also implement it.
What it is measured against
The indicators are set before anything is installed.
A pilot without a baseline is no basis for a scale-up decision. These are the indicators the work is designed against, measured before starting so there is something to compare with.
Alarms per operator per shift
The indicator that says whether the control room is operable. Measured before anything is changed.
Percentage of alerts acted on
How many ended in a recorded action, which is the only proof the alert was worth sending.
Detection time
How long passes between the condition appearing and the system raising it.
False positives
Notices that should not have fired, because they are what teach the operator to ignore the system.
What is the difference between an alarm and an alert?+
A classic alarm is deterministic: if pressure exceeds the limit, it sounds. A contextual alert crosses that variable with vibration, temperature, operating regime and history to say how serious it is, what the probable cause is and what to do. In practice, the first informs and the second lets you prioritise.
How do you reduce alarm overload?+
With deduplication, grouping by event, suppression of those that are consequences of another, severity that depends on plant state and not only on a threshold, and recurrence analysis to attack the ones that keep repeating. The aim is not to notify more: it is that whatever arrives demands action.
Can anomalies be detected without a labelled failure history?+
Yes, and it is precisely the most common industrial case. Unsupervised methods learn the asset's normal behaviour and flag the deviation, without needing examples of the failure. They do require a prior observation period to build that reference.
What does an OT data platform need to connect to what already exists?+
Connectors towards the protocols the plant already speaks — Modbus, OPC UA, MQTT, historians — and a contextualisation layer that ties each signal to its asset, its unit and its process. Replacing the existing OT infrastructure should not be a requirement to start.
Is an AI copilot useful in a plant?+
It is useful when connected to the real state of the operation: manuals, procedures, alarms, historian, CMMS and past failures. An isolated copilot that only answers general questions adds nothing over what can already be searched; a contextual one can become the operational interface.
Contact
Let's talk.
If you have an operational challenge, an innovation project in mind or a question about applying technology to your industry, get in touch.
Synapse Group