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Custom Manufacturing Software, Data & Applied AI
For the process no product on the market models and the questions no dashboard answers. Built on your data, for your floor, by the people who mapped it.
When does a manufacturer need custom software rather than a platform?
When the process is genuinely specific to the business, and configuring a general platform around it produces something people work around.
A routing, a planning constraint or a customer-mandated workflow can be like that. Custom is the right answer less often than software firms claim and more often than platform vendors admit.
Data first. Everything else depends on it.
Most failed manufacturing analytics projects did not fail at the model. They failed because the data underneath was inconsistent, incomplete across shifts, or defined differently by each system that produced it. We build the platform before we build on top of it.
Unified data platform
Machine telemetry, operator entries and enterprise records in one model, with one definition of a part, a line, a shift and a batch.
Context
Raw signals joined to what was running, who was on shift, which material lot and which order. Without that, no model has anything to learn from.
Quality and lineage
Completeness checks, gap detection and traceability from any number back to the sensor or the person that produced it.
AI in manufacturing: where models earn their place
A model is worth building when a person makes the same decision repeatedly, there is enough labelled history to learn from, and there is a clear action when the model is right.
Predictive maintenance
Condition data against failure history, on assets that fail often enough to have taught you something. Most useful on rotating equipment and on assets where an unplanned stop cascades down the line.
Quality prediction
Process parameters against inspection outcomes, flagging a drifting batch before final inspection. It works where there is parametric data and a recorded defect history.
Anomaly and drift detection
Catching the condition nobody wrote a rule for, on processes stable enough that normal can be defined. Often the best first model, because it needs no failure labels.
Plain-language access to plant data
Letting a supervisor ask a question in words instead of learning a reporting tool, grounded in your data model so the answer shows its working.
Demand forecasting on a short history. Predictive maintenance on an asset that has failed twice. Computer vision for a defect your inspectors already catch reliably. Any model whose output has no owner and no defined action. These demo well and are quietly abandoned soon after go-live.
Built for the process that is actually yours
Where a platform would need so much configuration that it becomes bespoke anyway, only harder to change.
- Operator and supervisor interfaces designed for gloves, glare and short windows
- Planning and scheduling tools that model your real constraints
- Customer and supplier portals where a contract requires you to share data
- Bespoke operational applications for processes no vendor has met
- Reporting and control-tower views built around your review meetings
- Integration services that hold a mixed estate together
Common questions
What do we own, and what do we license?
Both models exist, and we are explicit about which applies before anything is built. Bespoke software commissioned as development work is yours, source code included. Where a solution runs on our own platform components, those carry a licence like any product. We will not blur the two.
How much data do we need before AI is realistic?
It depends more on what you are predicting than on volume. Anomaly detection can be useful within weeks because it only needs to learn what normal looks like. Failure prediction needs enough examples of the failure, which on a reliable asset can take years. We will tell you which case you are in before anyone budgets for a model.
Can you work with our existing data warehouse or cloud?
Yes, and we prefer to. Building alongside the platform you have already standardised on is lower risk and cheaper to run than introducing a parallel stack.
What happens if you are not available?
We hand over source code, documentation and architecture as the work proceeds rather than at the end, and we build on mainstream technology that your own team or another firm can pick up.
Start with the audit, not the software.
A fixed-scope diagnosis of where your plant loses information, what it costs you and what to fix first. The roadmap is yours to keep, whoever builds it.