Production visibility and control
Production status, throughput, schedule adherence, work in progress, downtime, yield, scrap and overall equipment effectiveness where the underlying definitions are agreed.
Manufacturing
Practical software for production, maintenance, quality and material flow.
Connected Intelligence links the data and workflows around the plant so that operational teams can see what is happening, act sooner and report from a consistent basis.
We work with the systems already in place and build what is missing between them.
The plant is not a blank sheet
Manufacturers operate mixed estates: ERP, MES, WMS, QMS, maintenance systems, machine and historian data, SQL databases, spreadsheets and processes that remain manual because they cross formal system boundaries.
The design must also survive shift patterns, line changeovers, imperfect master data, operator time constraints, planned shutdown windows and the practical separation between operational and information technology.
Connected Intelligence works with that reality. We build the integrations and operational applications needed to give plant, engineering, quality, logistics and management teams a coherent view of the work.
Where we help
Quality and traceability
Defect capture, first-pass yield, non-conformance, quality holds, rework, root-cause workflows, inspection records and traceability across product, batch, line or asset.
Maintenance and asset health
Asset histories, inspections, work orders, planned maintenance, failure records, alerts, maintenance backlog and condition-based decision support.
Warehouse, fleet and material flow
Pick performance, stock and location visibility, material movement, warehouse-capacity views, transport defects, fleet performance and delivery operations.
Data integration and operational MI
Integration across ERP, MES, WMS, QMS, CMMS, historians, databases, APIs, files and spreadsheets; governed pipelines and operational dashboards.
Operator and supervisor tools
Focused applications for shop-floor capture, escalation, handover, checks, approvals and local decision-making without unnecessary administrative burden.
Predictive systems
Anomaly detection, demand and throughput forecasting, failure-risk models and AI-assisted operational decisions where data quality and business value justify them.
Definitions before dashboards
A dashboard is only useful if each measure has an agreed definition, source, refresh cadence, owner and treatment of missing or late data.
We establish that basis before visualising performance. This prevents a familiar outcome: a technically polished dashboard that each function interprets differently and no one is prepared to use for a material decision.
The objective is a common operating view, not another reporting surface.
Representative experience
The experience behind Connected Intelligence includes multi-source data pipelines and operational reporting across manufacturing, automotive and transport environments.
This has included production and picking measures, vehicle and defect data, cost information and reporting assembled from spreadsheets, SQL sources and relational systems. The work replaced repeated manual aggregation with scheduled pipelines and operational views designed around the decisions teams needed to make.
Brownfield delivery principles
Integrate before replacing
We identify which systems remain authoritative, which gaps require new software and where a replacement case is genuinely justified.
Protect production continuity
Testing, deployment and migration are planned around operating constraints. Failure and fallback behaviour are considered before release.
Respect the IT and OT boundary
Machine, control and business-system integrations are designed with clear ownership, security and support responsibilities.
Design with the people doing the work
Operators, supervisors, engineers, maintenance, quality and logistics teams see different parts of the process. The system must reconcile those perspectives rather than privileging a board-level process map.
Make data quality operational
Missing codes, inconsistent asset identities and poorly defined measures are not treated as abstract data problems. They are resolved in relation to the decisions and workflows they affect.
AI after the data foundation
Predictive maintenance and anomaly detection become credible only when asset identity, operating context, event history, failure labels and maintenance records are sufficiently reliable.
We establish that foundation first. Models are then validated against real operating cases, deployed within a usable workflow and monitored as conditions change.
Built for the people accountable
We work with manufacturers, tier suppliers, industrial service providers, warehouse and logistics operations, and engineering-led production businesses.
Typical sponsors include managing directors, operations directors, plant managers, manufacturing and engineering leaders, heads of maintenance, quality directors, supply-chain leaders and IT or digital teams.
Improve the flow of information around the plant.
Bring us the production, quality, maintenance or logistics process that is still being held together manually between systems.