Software for plants that cannot afford surprise downtime
Manufacturing
A plant runs on machines, and machines generate more data than most manufacturers ever use. This practice turns that data into fewer surprises: less unplanned downtime, faster root cause, and systems that finally agree on what actually happened on the floor.
The plant, made visible
How manufacturing data pipelines and predictive models actually get built.
90 secSample frame · video pending01Start from the floor
Name what the plant is actually costing you.
Invisible data, repeating downtime, systems that disagree, or a control layer nobody dares touch: these are the four pressures that bring manufacturers here. Pick the one draining your week and the map answers with the practices built for exactly that.
Machine and line data exists but nobody can see it in time to act.Manufacturing analytics gets sensor and MES data into one governed view, and AI turns that view into predictions worth acting on.
The same failures recur and maintenance stays reactive.Predictive models trained on your own failure history catch the pattern before the line stops, backed by the data pipeline underneath them.
Production data does not reconcile with the systems finance and planning trust.Custom engineering builds the missing bridge, and system integration keeps every platform working from the same production truth.
The MES or control layer is old enough that nobody wants to touch it.Staged modernisation changes the plant system without a production stoppage, one reversible wave at a time.
Manufacturing Data & Analytics
Shop-floor and MES data unified into one governed view.
AI for Manufacturing
Predictive maintenance and quality models trained on your line.
Custom Software Development
Purpose-built systems for workflows no MES vendor covers.
System Integration
MES, ERP and shop-floor systems made to agree with each other.
Legacy Modernisation
Ageing plant systems changed in reversible, production-safe steps.
Security & Compliance
The security engineering connected plant systems must stand on.
02Built for the production line
Engineered so the floor never has to wait.
Manufacturing software fails when it demands downtime nobody can afford. This delivery system is built around live production, with every stage closing on evidence a plant manager can inspect without shutting anything down.
Instrument before automating
Sensors, MES exports and manual logs are mapped to the decisions they should inform. Automation targets the gap that actually costs money.
A production map, not a guessBuild on your own history
Predictive models train on your equipment's real failure patterns, not generic benchmarks. A model that ignores your machines is a demo, not a tool.
Models trained on your lineRun in parallel until proven
New pipelines and dashboards run alongside existing reporting until the numbers agree. Nothing replaces a trusted report before it earns that trust.
Deployed beside the old systemHand over the running platform
Documentation, training and a support handover move ownership to your team on a planned schedule, so the platform outlives the engagement.
A system your team owns
03What plant and operations leaders commission
Manufacturing services by the name a plant leader searches for.
A plant leader searches for MES integration, predictive maintenance or an ERP bridge, not for software in general. The rows match each of those to the deliverable, the industrial standards it must speak and the practice that builds it.
| What you need | What we build | Standards and integrations | Where it lives |
|---|---|---|---|
| MES integration and MES-adjacent applications | Work order, traceability and quality applications connected to the MES rather than replacing it | ISA-95, ISA-88 batch models, OPC UA, Siemens Opcenter and Rockwell Plex APIs | Custom Software Development |
| Shop-floor data and analytics platforms | Sensor, PLC and MES data unified into one governed pipeline with OEE and downtime views | OPC UA, MQTT Sparkplug B, ISA-95 data models, AWS IoT SiteWise | Manufacturing Data & Analytics |
| Predictive maintenance and quality AI | Failure and defect models trained on your equipment history and evaluated before touching a live decision | ISO 13374 condition monitoring, OPC UA, MQTT, ISO 9001 quality records | AI for Manufacturing |
| ERP and plant integration | One production truth reconciled across ERP, planning and the floor through documented interfaces | SAP S/4HANA and SAP PP-PI, Oracle Fusion, ISA-95 Level 3 to Level 4 exchange, EDI | System Integration |
| Legacy plant software modernisation | Ageing MES, SCADA and control-adjacent systems changed in reversible, production-safe stages | OPC Classic to OPC UA migration, IEC 61131-3 PLC standards, ISA-95 | Legacy Modernization |
| OT and IT security for connected plants | Segmented networks, access control and monitoring designed for the OT environment rather than retrofitted | IEC 62443, NIST SP 800-82, Purdue model segmentation, MQTT with TLS | Cybersecurity Hub |
ISA-95
The standard defines the levels and data exchanges between business planning and plant control, so integration contracts between ERP, MES and the floor are drawn against its models from the first workshop.
OPC UA
The vendor-neutral protocol carries machine and PLC data with authentication and encryption built in, so pipelines read equipment through it wherever the controller supports it, and adapters cover the rest.
SAP S/4HANA
Production planning, inventory and costing live in the ERP, so plant integrations post confirmations and consumption through SAP's published APIs rather than direct table writes.
IEC 62443
The series sets security zones, conduits and levels for industrial control systems. Network segmentation, access control and monitoring for connected plant equipment are designed to its requirements rather than an IT template.
04Evidence and proof
Plants that stopped repeating their failures.
Manufacturing proof is measured uptime and reconciled numbers, not dashboards nobody trusts. These previews show that standard while written client approvals are collected.
Explore case studiesUnplanned downtime cut through predictive maintenance.
A mid-size manufacturer lost production days to recurring line failures. Models trained on two years of maintenance logs flagged failures before they stopped the line.
- Unplanned downtime
- Cut significantly
- Failures caught in advance
- Majority of recurring cases
MES and ERP finally agreeing on one production number.
Nexa Foundry ran finance and planning on exports that never matched shop-floor reality. An integration layer gave both sides one governed source.
- Production data sources
- Unified into one
- Monthly reconciliation effort
- Cut sharply
They spent the first month on our sensors before they wrote a line of the dashboard. That order mattered.
Sample · pending client approval
05Three doors into plant work
Pick the door that fits the production risk.
Some plants need a data foundation first, some need a specific system built, and some need a broader modernisation of ageing control software. Every door below runs on the same uptime-first discipline, so the choice is about scope, never about risk to the line.
- Project Scoping
Scope the production problem
A priced engagement that maps your data sources, failure history and integration gaps before any build commits.
- Custom Software Development
Build the plant system
Custom engineering for the shop-floor workflow no MES vendor has solved, delivered around your production schedule.
- AI & Automation
Bring predictive models in
AI applied to maintenance and quality, trained on your equipment and evaluated before it touches a live decision.
06Straight answers
Asked before plant floor budgets commit.
01Do you work with our existing MES vendor?
Regularly, and the goal is never to replace a working MES. Integration and data extraction work around it, and custom development covers only the workflows the MES genuinely does not serve. Recommending against a replacement is a normal outcome of the assessment.
02How do you build predictive models with limited failure data?
Sparse data changes the approach, not the honesty of the result. Physics-informed models and conservative thresholds work where pure data-driven models would overfit, and the model's confidence is reported plainly rather than dressed up as certainty it does not have.
03Can this work without touching the production line?
Yes, and that is the default posture. Data extraction, dashboards and predictive models run alongside existing operations. Anything touching the control layer itself moves through a staged, rehearsed process with maintenance windows agreed well in advance.
04What does manufacturing software development cost?
Scoping is fixed-price and maps your specific sensors, systems and failure history into a costed plan. Ongoing model operation is priced separately from the build, so you see the full cost of keeping predictions accurate over time.
05Do you handle OT and IT security together?
Yes, because connected plant systems create exposure on both sides. Network segmentation, access control and monitoring are designed for the OT environment specifically, not retrofitted from a generic IT security template.
06Can you take over a stalled digitisation project?
Yes, starting with an honest read on why it stalled rather than a fresh pitch. Most stalled plant projects fail on data quality or unrealistic scope, and the assessment names which one applies before recommending a path forward.
07How fast can a manufacturing project show results?
A data foundation typically lands within a quarter, since sensor integration and pipeline work can proceed without waiting on a full platform. Predictive models need enough historical data to train responsibly, which the scoping stage estimates honestly up front.