Plant-floor systems
and the AI on top of them.
MES and ERP modernization, OT/IT integration, predictive maintenance and quality AI, and the plant-floor data unification that all of it depends on. Engineered for the equipment data your operators have been generating for years.
What manufacturing software looks like in practice.
Manufacturing software is two stacks bolted together: the OT side (PLCs, SCADA, historians, MES) that runs the plant floor, and the IT side (ERP, quality systems, supply chain) that runs the business. The integrations between them tend to be old, point-to-point, and held together by a handful of people who understand both worlds.
Most operators don't need to replace either stack. They need the data trapped inside the historian to be query-able from the warehouse, the quality data to flow back into the MES, the predictive-maintenance signals to reach the people who can act on them, and the AI work that's now possible against equipment data that's been accumulating for a decade. The hard part is bridging the two stacks without breaking either one.
Apollo builds the modernization, OT/IT integration, and AI work that lives across both stacks. Plant-floor-aware by default, integrated with the historians and MES you already run, and architected so the IT-side analytics don't break when the OT-side network goes intermittent.
Plant-floor systems run 24/7 and the consequences of failure are measured in production hours. Anything Apollo ships into that environment is engineered to fail gracefully: store-and-forward queues for OT/IT links, idempotent writes, and explicit fallback paths when an upstream system isn't reachable.
The custom Excel-and-VBA spreadsheet that runs production scheduling. The historian-to-warehouse export that runs nightly and fails silently when a tag schema changes. The MES customization layer that nobody on the current team wrote and nobody is willing to modify.
Predictive maintenance against equipment data you already collect. Quality-anomaly detection against vision or process data. Production scheduling assistants that work with your existing constraints. Documentation and procedure assistants for the floor. AI is useful when the data is already there and the recommendation is reviewable.
Four engagement patterns in manufacturing.
Most manufacturing engagements land in one of these shapes. Each gets built plant-floor-aware, with the reliability posture the OT environment requires, and integrated with the historians, MES, and ERP you already run.
MES / ERP Modernization
The applications that sit around the MES and ERP and do the work neither of them does well: production scheduling tools, quality-data interfaces, operator UIs, and the integrations that move data between the two stacks. Modernized in place, with the reliability posture the OT environment requires.
OT / IT Integration
The bridge between the plant-floor systems (PLC, SCADA, historian, MES) and the IT-side data and analytics layer. Store-and-forward, idempotent, and observable. Built so the IT-side warehouse stays consistent when the OT-side network is intermittent, and so a historian schema change doesn't take down the analytics.
Predictive Maintenance & Quality AI
Machine-learning models against the equipment data you've been collecting for years: vibration, temperature, current draw, vision data, and process signals. Predictions that reach the people who can act on them, with the explanation and confidence scoring that makes them reviewable on the floor.
Plant-Floor Data Unification
Bringing historian, MES, quality, and ERP data into a single query-able layer. Tag-to-schema mapping, identity resolution across the systems, and the lineage required for traceability and regulatory submissions. The foundation for everything downstream, including the AI work.
Where our work sits.
Manufacturing operators run two stacks: OT for the plant floor, IT for the business. Apollo builds the integration, modernization, and AI work that lives across both, without forcing one stack to look like the other. The map below is simplified, but representative.
Standards and regulations that shape what we build.
The frameworks that shape what we build, and what we ship.
Manufacturing has fewer cross-cutting privacy regulations than healthcare or financial services, but the standards landscape is just as dense. Industry-specific regulations apply where the product is regulated (food, pharma, medical devices, defense), and interoperability standards govern almost every integration.
Apollo treats these as design inputs from the start. Genealogy, lineage, validation evidence, and electronic-records posture are specified before the build begins and reviewed against the relevant standards throughout.
The panel on the right is the working set we encounter most often. Any given project picks a subset, and the proposal will be explicit about which ones apply and what we will and won't certify directly.
Sector-specific regulations
Quality & safety standards
Interoperability & data standards
Four phases. Built around plant-floor reality.
Apollo's standard methodology, applied to manufacturing. OT reliability posture, tag mapping, and lineage requirements get specified before the build starts, so the IT-side analytics don't fall over when the OT-side network goes intermittent.
Map the floor. Map the IT side.
The current OT stack (PLC, SCADA, historian, MES), the current IT stack (ERP, quality, supply chain), the integrations between them, and the volumes. The standards and regulations in scope. Where AI helps, where rules suffice, and where an operator still has to confirm.
Architecture, reliability plan, lineage plan.
System architecture and OT/IT integration design. Reliability posture for the OT-side links: store-and-forward, idempotency, network-fault tolerance. Lineage and traceability design for the regulated workflows. Operator-UI wireframes where humans stay in the loop.
Bridge. Platform. Operator UIs.
OT/IT bridge deployed, historian and MES integrations connected, predictive models wired up, operator UIs functional. Two-week iterations. Each shipped capability arrives with its tests, its monitoring, and the lineage fields traceability will ask for.
Measure. Tune. Expand.
OEE, yield, throughput, model accuracy, and OT-link availability in production dashboards. Threshold tuning based on what the data shows. Gradual rollout to adjacent lines or plants once the first one is steady. Knowledge transfer to your team along the way.
Tell us what you're trying to build.
Send a paragraph about the project: the OT and IT systems involved, the volumes if you have them, the standards in scope, and the parts that aren't working today. We'll reply within one business day, either with a 30-minute call or with an honest "this is not the right fit; here's who you should call instead."