Trigger Data designs, builds and operates production data platforms, applied analytics and AI, and custom software for asset-intensive and regulated organisations. We connect fragmented data with operational context and put it into workflows people can use.
Trusted in complex, regulated and operational environments
From high-volume machine data to dependable production healthcare platforms, our work connects the evidence, operational context and software needed to change how a client works.
Most clients arrive with one of four situations. We define the user outcome first, then bring together the data, AI and software disciplines needed to deliver it — without imposing a pre-selected stack.
Frequent incidents, risky releases, opaque cloud cost or a platform nobody wants to change.
We stabilise the platform as one production system: architecture, deployment, observability, data quality and cost controls are improved together. The aim is not simply fewer incidents, but a platform that teams can change confidently and use to deliver new capability faster.
Telemetry, maintenance, geography, documents and business systems describe different parts of the same event.
We connect operational datasets around the real-world event, asset or decision they describe. Telemetry, maintenance records, geography, documents and business information are brought into a governed model and presented through a workflow that helps users move from a fleet-level pattern to the supporting evidence.
A model, dashboard or AI prototype exists, but it is not governed, monitored or embedded in everyday work.
We turn analytical logic and AI prototypes into governed production workflows. That means connecting them to dependable data, defining how people review and act on the output, and building the monitoring, evaluation and support needed to operate them safely over time.
The initial release is only the start and the organisation needs accountable engineering beyond launch.
We stay accountable after the first release. Production support, monitoring, incident response and planned improvements are treated as part of the service—not as an afterthought. Live evidence and user feedback then guide where the system should be strengthened or extended next.
Asset-heavy operations carry our strongest public evidence. The same engineering approach—connecting operational data, context and software—also supports healthcare, public services and other asset-intensive organisations.
Rolling stock, route, maintenance and operational data brought together for performance, reliability and investigation.
Secure, auditable platforms and operational applications where performance, governance and continuity are critical.
Equipment, maintenance, location and operating context connected to improve reliability and execution.
We reduce delivery risk by agreeing the operational decision first, putting representative working evidence in users’ hands early, and treating production support as part of the solution from the outset.
Agree the user, decision, evidence, constraints and success measure.
A prioritised problem and testable first slice
Connect representative data to working logic and a usable interface.
Working evidence in users’ hands
Release into a supported environment and use live feedback.
A dependable service that improves with use
Senior engineers stay close to delivery, taking responsibility from source data and operational logic through the application, deployment and live support.
The people shaping the solution remain close to delivery.
Source data, engineering logic, application, deployment and support are treated as one problem.
Security, observability, maintainability and cost are designed in.
The smallest complete capability is preferred over unnecessary platform building.
A product route for asset reliability. AssetBeats connects maintenance, condition and operational data so teams can identify emerging problems, investigate causes and focus action where it matters.
Describe the operational decision, workflow or production problem that is getting in the way. A Trigger Data partner will review it and arrange a focused working session.
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