Manufacturing software that connects the factory floor to your ERP

We work through each ERP's supported integration path: the Service Layer and DI API for SAP Business One, the contract based REST API and OData interface for MYOB Acumatica, SuiteTalk and RESTlets for NetSuite, and the published APIs for Microsoft Dynamics 365 Business Central and Finance and Operations. Older on-premises ERPs without a usable API can often be integrated through their database views, scheduled exports or an existing middleware layer. We confirm the exact route, licence requirements and any partner involvement during the Discovery Audit, before you get a fixed price.

Usually build around it. An ERP is expensive and disruptive to replace, and most of the pain manufacturers describe comes from the gaps between the ERP and everything else, not the ERP itself. We keep the ERP as the system of record for orders, stock and costing and build the missing pieces beside it: shop floor capture, quality workflows, traceability and reporting. If your ERP genuinely can't support where the business is going, we'll say so and help you scope a replacement with an ERP partner rather than rebuild it ourselves.

Yes, this is one of the most practical uses of AI in manufacturing. A document processing pipeline reads incoming PDFs and scans, extracts fields such as supplier, PO number, batch or heat number, quantities and test results, checks them against the purchase order and specification in your ERP, and files the document against the right receipt. Each extracted field carries a confidence score, so staff review only the uncertain values and anything that fails a specification check, rather than retyping every document.

We build the data foundation that predictive maintenance depends on: collecting machine and sensor data, linking it to assets, jobs and maintenance history, and making it queryable. On top of that we can build condition alerts and anomaly detection. Whether prediction is worth it depends on having enough history, including failures, for the machines that matter. We assess that honestly in the Discovery Audit, and often recommend starting with condition monitoring and better maintenance records before any prediction model.

Often, if it is scoped tightly. We start with our Discovery Audit, a fixed fee credited in full against the build, to find the one or two workflows where connecting systems or removing retyping saves the most time, then build those at a fixed price. You own what we build for you once it is paid for, and the full repository and logins are handed over after the final payment. We can host the system in Australian cloud regions or on your own infrastructure, and we document it so your team or another provider can support it. Some manufacturing R&D may be eligible for the R&D Tax Incentive; speak to a registered R&D tax agent, as we are not one.

Yes, if it is designed for it from the start. Shop floor apps should work offline and sync when the connection returns, support shared tablets with quick badge or PIN sign-in, use large touch targets that work with gloves, and scan barcodes or QR codes rather than relying on typing. We test on the actual devices and in the actual environment before rollout.

It depends on how many systems are involved and how much the shop floor needs to change, so we fix the price after our Discovery Audit, a fixed fee credited in full against the build. For typical Australian market ranges, with the publishers named, see our custom software development cost and AI development cost guides. Those are market figures, not our prices. Most manufacturers start with one workflow rather than all of them.

A manufacturing execution system (MES) manages and records work on the shop floor: scheduling, work orders, data capture, quality and traceability. Large or highly regulated plants often justify a full MES. Many midsize manufacturers get most of the value from a few targeted apps connected to the ERP, covering scheduling, shop floor capture and batch traceability, at lower cost and disruption. We compare both routes honestly in the Discovery Audit.

For equipment that exposes data, yes. Many modern machines and PLCs publish data over industrial protocols such as OPC UA or MQTT, often through an edge gateway, and older machines can be retrofitted with sensors. We build the software side: collecting that data, timestamping it, linking it to jobs and assets, and making it available to dashboards and maintenance workflows. Any controls or electrical work on the machines themselves stays with your automation integrator or electrician.

Software that answers one question fast: which batches contain this ingredient, and where did they go? FSANZ Standard 3.2.2 requires a written recall plan, which needs batch codes, distribution lists and a record of food unaccounted for. Most makers run an ERP or MRP such as Unleashed, Katana, MRPeasy or Business Central, and a practical goal is a one up, one down trace for any lot within an hour. Custom work fills the tracing gaps.

Yes, and the right replacement depends on what the file does. If a SaaS product fits, use it; low code tools such as Power Apps suit small internal tools for teams on Microsoft 365; a custom web app fits when the file holds rules that set you apart or must connect to your ERP. A custom replacement typically costs $15,000 to $40,000 (AUD, ex GST) for a simple tool and $40,000 to $150,000 for a system with several users and integrations.

Yes, as a check rather than a sign off. Software can compare draft label artwork with the recipe and ingredient specifications, confirm every allergen appears with its required name in bold in the ingredients list and in a summary statement starting with Contains, and flag anything missing. Recipe changes and new ingredient lots should trigger a label review. A qualified person still approves the final artwork against the FSANZ allergen labelling rules.