Manufacturing Data Integration Services for Real-Time Operations

It connects everything that runs the plant and the business — ERP, finance, and planning; MES, SCADA, and PLCs on the floor; IoT and machine sensors; warehouse, inventory, and supplier files; quality and maintenance records — into one trusted source the floor and the office both read from. The hard part is rarely the modern tools; it’s the aging controllers, proprietary protocols, and undocumented logic that off-the-shelf connectors stall on.

Integration is the foundation; analytics is what you build on top. Integration pulls data from every plant system into one clean, trusted source; analytics turns that source into dashboards, KPIs, and forecasts. Built on un-integrated data, analytics inherits every silo underneath it — so the integration layer decides whether the numbers can be trusted at all.

Each layer speaks its own language: ERP and MES hold business and production records, while SCADA and PLCs emit machine signals over protocols like OPC-UA, Modbus, and MQTT. We pull the machine layer through edge gateways, streaming in under a minute, capture ERP and MES changes through managed connectors, and land it all in one governed model with full lineage from sensor to report. Every record is checked on the way in, so a missed reading or duplicate timestamp can’t quietly corrupt a number downstream.

It means plant events reach your dashboards as they happen — through streaming pipelines, not a nightly export. A supervisor can act on a machine event during the same shift, instead of reading about it the next morning. We build these pipelines as real production systems, with retries, validation, and room for load spikes, so one bad batch never takes the feed down.

Predictive maintenance needs a steady, trustworthy stream of machine and sensor data tied back to its source — and that’s what the integration layer produces. We deliver that clean, lineage-tracked foundation; your data-science team trains the models on it, or we build them together. We own the data layer end to end; the models sit on top.

AI is only as reliable as the data under it. Once plant-floor and business data are unified, cleaned, and lineage-tracked, that foundation is ready to train models — anomaly detection, forecasting, predictive maintenance — with no fresh data-prep scramble for each project. The model earns trust because it learns from numbers the business already trusts.

It depends on how many systems you have, how old they are, and how well documented — but the shape is the same every time. We start with an audit, then connect the systems that unblock the most value first, so you see a result early instead of at the end of a long program. Old systems migrate in parallel and we retire them only after validation, so reporting never goes dark mid-project. We commit to a timeline once the audit shows what’s really there.

Cost tracks complexity, not a price list: how many systems, how many are old or undocumented, the data volume and speed you need, and how much governance the work takes. A four-system batch job and a 20-system real-time platform are worlds apart. We quote after the audit, so the number fits your actual estate — and that first audit is free.

Those are platforms you still design, run, and maintain yourself; we deliver the outcome — architecture, pipelines, and the people who operate them. A generic iPaaS assumes clean, documented sources, so on a plant floor it stalls on the proprietary protocols, undocumented logic, and aging controllers we are built to handle. And we are not tied to one platform — we pick Databricks, Snowflake, a lakehouse, or a plain warehouse to fit your workload, not because we resell it.

Consultancies bill for headcount and hand the build to rotating juniors; local integrators rarely stay past go-live. One senior team designs, builds, and keeps running your platform, so the knowledge stays with your data, not with a contractor who leaves. If something breaks, you reach the engineers who designed it, not a ticket queue.

That is exactly what we are built for: dozens of systems, often 10 to 15 years old, with custom extensions and undocumented logic. We start with an inventory — every source mapped before we touch it — so you see what you have and where the risk sits, then pick the first integration that unblocks the most value. From there we migrate legacy ERP, MES, and SCADA in parallel and retire them only after validation, so reporting never breaks mid-modernization.

Yes. In production today, a live operations map we built refreshes a heavy-equipment fleet’s 50+ assets at five-second cadence, so operators see a machine’s status the moment it changes. We are rolling out sub-five-minute PLC streaming across a 12-site lakehouse so supervisors act on plant events inside the shift, not the next morning. The cadence is set by the decision — a safety interlock needs sub-second, an OEE dashboard does not — and we engineer fault tolerance and load-spike headroom into both.