The value of smart factory technology — robotics, IoT sensors, automated production lines — is constrained by the software layer that connects and coordinates it. Automation islands operate without shared visibility. IoT data is collected but not acted upon. AI models exist in isolation from production workflows. Perimattic builds smart factory software platforms that integrate robotic systems, IoT devices, production automation, and AI-driven optimisation into a coherent production intelligence layer — connecting the physical factory to the data and decision-making systems that make it genuinely smart.
Smart Factory Technology Stack — Node.js, Python, OPC-UA, MQTT, AWS, Docker, React, Kafka, TypeScript, TensorFlow, REST APIs, Kubernetes
A smart factory is a connected production environment where machines, sensors, software, and AI make production decisions with minimal manual intervention. It is not defined by the presence of robots or automation — it is defined by the degree to which those automated systems are connected to a shared information layer that provides visibility, enables coordination, and supports AI-driven decision making across the factory as a whole. The software layer is what distinguishes a smart factory from a factory with automation: it is the platform that connects robotics, IoT sensors, production equipment, and enterprise systems into a coherent environment where the output of one system informs the operation of another.
The challenge that most manufacturing enterprises face is the automation island problem. Robotics cells, CNC machines, conveyor systems, and production equipment each operate effectively in isolation — but without a software layer that connects them, their data remains siloed. IoT sensors generate valuable signal but without processing and integration infrastructure, that signal does not reach the production workflows where it could change decisions. AI models are deployed into analytics tools that produce recommendations that sit in dashboards while the production floor operates on manual judgement. The result is a factory that has invested heavily in automation and technology but has not yet captured the compounding value that comes from connecting those investments into a unified intelligence layer.
Perimattic builds smart factory software platforms that create this integration layer. Every engagement begins with a thorough discovery of the actual automation landscape — what equipment exists, what protocols it speaks, what data it can provide — before any platform architecture is designed. We build the connectivity layer using OPC-UA, MQTT, and industrial protocols that handle real factory network conditions. We deploy AI models that are integrated into the MES and production workflows where decisions are made. And we structure engagements in phases, validating each integration before building the next layer on top of it.
Production visibility
Each automation system visible only in its own interface — no unified production view across the factory floor
Production visibility
Unified production intelligence dashboard across all systems — real-time visibility of every production zone, machine, and IoT device in a single platform
Decision making
Production decisions made manually based on lagging reports and operator knowledge — not on the current production state
Decision making
AI-assisted decisions driven by real-time production state — scheduling adjustments, quality interventions, and maintenance actions triggered by live data
Machine coordination
Automated systems operating independently with manual handoffs between stations — no coordination across robotics, conveyor, and assembly
Machine coordination
Coordinated production flow across robotics, conveyor, and assembly driven by the smart factory platform — automated handoffs and exception management
Quality integration
Quality checks performed at end of line — defects identified after production has continued, increasing rework and scrap costs
Quality integration
Quality data from vision systems and sensors integrated into production flow in real time — anomalies trigger parameter adjustments before defective material accumulates
Energy management
Energy consumption unmonitored at asset level or measured only at utility meter — no visibility of consumption by machine, line, or product
Energy management
Asset-level energy monitoring with production-correlated analysis and AI optimisation recommendations — reducing energy intensity and supporting ESG reporting
The cost of disconnected automation becomes visible in the gap between production performance and what the automation investment should theoretically deliver — excess scrap, unplanned downtime, manual coordination overhead, and the management time required to get accurate production data from systems that do not share it.
Seven smart factory capability areas covering the complete production intelligence stack — from platform development and robotics integration through AI, IoT, monitoring, energy management, and enterprise system connection.
Unified software platforms connecting automation, IoT, AI, and production management into a single factory intelligence layer. Built around the specific automation landscape, protocols, and integration requirements of the manufacturing environment — not around the feature set of a packaged product.
Software integration with robotic systems, PLCs, and automated production cells via OPC-UA and industrial protocols. We map the automation landscape, design the integration architecture, and build the connectivity layer that makes machine data available to the smart factory platform in real time.
AI models for production optimisation, quality prediction, scheduling, and anomaly detection deployed at the factory software layer. AI recommendations are connected to MES and production workflows where decisions are made — not delivered as reports that require manual interpretation.
Comprehensive sensor connectivity covering machines, environment, energy, and material flow across the factory floor. We design the IoT architecture — edge processing, MQTT connectivity, time-series data storage — that turns raw sensor data into actionable production intelligence.
Live production dashboards, alert management, and production control interfaces for shift management and engineering teams. Built from event streams rather than batch data exports — providing production state visibility that reflects the current condition of the factory, not last hour.
Factory-level and asset-level energy monitoring, consumption optimisation, demand management, and ESG reporting. We connect energy meters and power monitoring sensors to the smart factory platform and build the analytics layer that turns consumption data into optimisation recommendations.
Connection of factory intelligence platforms with MES, ERP, quality, and supply chain systems through structured API and event-driven interfaces. Production output, quality results, and energy data connected to enterprise workflows — enabling decisions at the factory level to propagate automatically to the systems that govern planning, procurement, and reporting.
A structured six-stage process from free factory operations discovery through production deployment and ongoing smart factory platform optimisation.
We map automation assets, IoT devices, production systems, OT network topology, connectivity landscape, and integration requirements. This free session produces a clear picture of the current automation landscape before any platform decisions are made.
We document all automation islands, protocols, data sources, existing integration points, OT network architecture, and target state architecture — producing a comprehensive baseline for the smart factory platform design.
We design the unified software layer and build a proof of concept validating integration with one automation system and one enterprise system, surfacing data quality and technical risks before full development begins.
We build the smart factory platform, integrate production automation, connect IoT devices, and deploy AI models into production workflows — delivering working capability at each phase rather than at project end.
We test across all integrated systems, validate AI model performance against defined KPIs, test edge cases in automation coordination, and run factory acceptance testing with operations and engineering teams.
We deploy with infrastructure monitoring, track AI model performance in production, support post-deployment stabilisation, and provide engineering capacity for ongoing factory evolution and new system additions.
We map automation assets, IoT devices, production systems, OT network topology, connectivity landscape, and integration requirements. This free session produces a clear picture of the current automation landscape before any platform decisions are made.
We document all automation islands, protocols, data sources, existing integration points, OT network architecture, and target state architecture — producing a comprehensive baseline for the smart factory platform design.
We design the unified software layer and build a proof of concept validating integration with one automation system and one enterprise system, surfacing data quality and technical risks before full development begins.
We build the smart factory platform, integrate production automation, connect IoT devices, and deploy AI models into production workflows — delivering working capability at each phase rather than at project end.
We test across all integrated systems, validate AI model performance against defined KPIs, test edge cases in automation coordination, and run factory acceptance testing with operations and engineering teams.
We deploy with infrastructure monitoring, track AI model performance in production, support post-deployment stabilisation, and provide engineering capacity for ongoing factory evolution and new system additions.
Select a production environment to see how we design, build, and integrate smart factory software platforms for manufacturing enterprises.
Automotive manufacturers operate complex multi-stage assembly lines where robotics, conveyor systems, and manual stations must be precisely coordinated — requiring smart factory platforms that provide unified production visibility and real-time coordination across every station.
Automotive manufacturers operate complex multi-stage assembly lines where robotics, conveyor systems, and manual stations must be precisely coordinated — requiring smart factory platforms that provide unified production visibility and real-time coordination across every station.
Electronics and semiconductor manufacturing demands sub-micron process control, strict lot genealogy, and contamination prevention — requiring smart factory platforms that integrate cleanroom equipment, automated material handling, and yield analytics into a coherent production intelligence layer.
Food and consumer goods manufacturers operate under strict food safety, allergen separation, and traceability requirements — requiring smart factory platforms that integrate production equipment, quality systems, and supply chain traceability into a compliant production intelligence layer.
Chemical and process manufacturing operates continuous or batch processes where process parameter control, safety interlock monitoring, and regulatory compliance are critical — requiring smart factory platforms that connect SCADA, DCS, and safety systems into a unified operational intelligence layer.
Discrete machining and fabrication operations require job shop scheduling, CNC machine monitoring, tooling management, and quality inspection coordination — making smart factory platforms essential for utilisation improvement and on-time delivery.
Automated warehouse and fulfilment operations — AMR fleets, conveyor sorters, AS/RS systems — require smart factory software that coordinates autonomous systems, monitors throughput, and integrates with WMS and OMS to maintain fulfilment SLAs.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate smart factory platforms built for real production environments from technology deployments that demonstrate in a lab but do not create operational value on the factory floor.
Every smart factory engagement begins with a thorough mapping of the actual automation landscape — the protocols in use, the data each system can provide, the OT network topology, and the existing integration points. We understand what the factory actually contains before designing software for it. This prevents the most common smart factory failure mode: a platform designed against assumptions about automation capability that the factory floor cannot deliver.
OPC-UA, MQTT, Modbus, and PROFINET integrations that handle the actual network conditions, protocol variations, and data quality of a factory environment — not the ideal conditions of a test lab. We design the edge layer, the data normalisation pipeline, and the error handling that makes industrial connectivity reliable under real factory operating conditions.
AI-generated recommendations that trigger actions in MES and production workflows rather than populating dashboards that require manual interpretation. We build the integration between AI model outputs and the production systems where decisions are made — scheduling, quality management, energy control, maintenance planning — so that factory intelligence creates operational change rather than interesting data.
The team that maps the automation landscape and designs the smart factory architecture also builds, tests, and deploys the platform. There is no handoff between a consulting team and a delivery team, no loss of factory context between discovery and implementation. You work with the same engineering team from the initial automation review through production deployment and post-launch support.
“A smart factory initiative that deploys sensors and AI models but does not integrate their outputs into the production workflows where decisions are made produces interesting data — but not a smarter factory. Intelligence only creates operational value when it is connected to the point where production decisions are made.”
Smart factory software solutions are platforms and systems that connect the physical elements of a manufacturing environment — robots, machines, sensors, automated production lines — to the data and decision-making infrastructure that makes production genuinely intelligent. A smart factory software platform integrates industrial IoT devices, robotic systems, production automation, and AI-driven optimisation into a unified production intelligence layer that provides real-time visibility, enables AI-assisted decision making, and coordinates automated systems across the factory floor. Unlike point solutions that automate individual tasks, smart factory software creates a coherent connected environment where production data from every system is captured, analysed, and acted upon in a coordinated way.
A traditional automated factory uses automation to replace manual labour in specific tasks — a robot that welds, a conveyor that moves parts, a CNC machine that cuts to specification. Each system operates independently and produces its own outputs. A smart factory connects these automated systems through a software layer that shares data across them, enabling coordination and AI-driven decision making that no individual system could provide alone. The key difference is not the presence of automation but the integration of that automation into a coherent information environment. In a smart factory, the production scheduling system knows the real-time status of every machine, the quality system informs the production plan, the energy management platform optimises consumption based on production load, and AI models generate recommendations that are acted on in production workflows — not reported in dashboards that require manual interpretation.
Smart factory software integrates with existing automation and robotics through industrial communication protocols designed for factory environments. OPC-UA (OPC Unified Architecture) is the primary protocol for connecting PLCs, SCADA systems, and modern industrial equipment — it provides a standardised, secure data model that allows a smart factory platform to read process variables, machine states, and alarm conditions from equipment regardless of manufacturer. MQTT is used for lightweight IoT sensor connectivity, particularly for edge devices and monitoring nodes distributed across the factory floor. Modbus and PROFINET are common in older and discrete manufacturing environments respectively. A smart factory integration project begins with a full mapping of the automation landscape — what equipment exists, what protocols it speaks, what data it can provide — before any platform architecture is designed. This discovery prevents the most common integration failure: designing a platform architecture against assumptions about what the automation can provide rather than against what it actually exposes.
AI in smart factory software serves several distinct functions that together constitute the intelligence layer of a connected production environment. Production optimisation models analyse real-time production state — current throughput, machine availability, material supply, order priorities — to recommend scheduling adjustments that maximise output against targets. Quality prediction models use process parameter data from sensors and production equipment to predict quality outcomes before material reaches end-of-line inspection, allowing parameter corrections before defective product is made. Anomaly detection models monitor machine behaviour patterns and flag deviations from normal operating envelopes that precede failures, enabling predictive maintenance interventions. Energy optimisation models analyse production load and energy consumption to recommend demand management actions. The critical distinction between AI that creates operational value and AI that produces interesting reports is integration: AI recommendations must be connected to the production workflows where decisions are made — MES, scheduling, maintenance, energy management — not presented in a separate analytics tool that requires manual action.
Industrial IoT connectivity in a smart factory platform is achieved through a layered architecture. At the edge, sensor nodes and IoT gateways collect data from machines, environmental sensors, energy meters, and production equipment and publish it over MQTT or OPC-UA to an edge processing layer. The edge layer filters, aggregates, and normalises raw sensor data before forwarding it to the cloud or on-premises data platform. A time-series data store — InfluxDB, TimescaleDB, or a cloud-native equivalent — captures the high-frequency sensor data at the appropriate resolution. A streaming processing layer, typically Kafka, handles real-time event processing for alert generation, anomaly detection, and trigger-based workflow actions. Above this sits the application layer: dashboards, AI models, and the integration interfaces that connect sensor-derived insights to MES, ERP, and production control systems. The architecture must be designed for factory network conditions — intermittent connectivity, OT/IT network separation, varying data quality — not for ideal cloud-connected environments.
Yes, and phased implementation is the standard approach for smart factory projects of any meaningful scale. A full smart factory platform connecting every system in a manufacturing environment is a multi-year programme — attempting to design and deploy the entire platform before validating any component carries substantial risk. Perimattic structures smart factory engagements in phases that each deliver operational value independently: a production monitoring phase that provides real-time visibility across production lines, an IoT connectivity phase that integrates sensor data from a defined set of machines, an AI phase that deploys a production optimisation or quality prediction model against validated data, and an enterprise integration phase that connects factory intelligence outputs to MES and ERP workflows. Each phase validates the integration approach and data quality assumptions before the next phase builds on them. This also allows the business to demonstrate operational value from each phase and build the case for subsequent investment based on demonstrated results rather than projected benefits.
Smart factory software supports energy management by providing the asset-level consumption data and production-correlated analysis that utility-level energy monitoring cannot deliver. At the data layer, energy meters and power monitoring sensors are integrated into the factory IoT platform to capture consumption at machine and production line level — not just at the building or facility level. This granular data is combined with production output data to calculate energy intensity per unit produced, enabling meaningful comparisons across products, lines, shifts, and time periods. AI optimisation models analyse consumption patterns in relation to production load, identifying demand reduction opportunities, optimal timing for high-energy processes, and inefficiencies that do not appear in aggregate consumption data. At the reporting layer, the platform generates ESG and sustainability reports using actual production and consumption data rather than estimates. This combination of granular monitoring, production-correlated analysis, and AI optimisation recommendations is what distinguishes a smart factory energy management capability from a standalone energy monitoring tool.
Smart factory software integrates with ERP and MES platforms through structured API and event-driven interfaces that connect factory intelligence outputs to enterprise decision-making workflows. MES integration is typically the most critical: the smart factory platform receives production orders from MES, reports actual production output, quality results, and consumption data back to MES in real time, and triggers MES workflow events — non-conformance creation, material issue confirmation, production completion — automatically based on factory floor events. ERP integration covers the enterprise layer: production order confirmation updating ERP inventory records, component consumption triggering materials management events, and energy and resource consumption feeding into cost accounting. The integration architecture must handle the data volume and frequency differences between factory floor systems — which generate high-frequency event streams — and ERP systems — which typically use transactional, lower-frequency interfaces. Perimattic designs the integration layer to aggregate, buffer, and transform factory data into the event types and frequencies that ERP and MES integrations are designed to receive.
OT/IT convergence — the integration of operational technology (factory floor systems, PLCs, SCADA) with information technology (cloud platforms, enterprise software, analytics) — introduces a set of security, network, and governance considerations that do not arise in pure IT projects. Factory OT networks are typically isolated from corporate IT networks for security reasons, and this separation must be maintained while enabling the data flows that smart factory software requires. The architecture typically implements a demilitarised zone (DMZ) between OT and IT networks with a one-way data diode or controlled gateway that allows data to flow from OT to IT without creating inbound attack vectors into the OT network. Protocol translation is required between OT protocols (OPC-UA, Modbus, PROFINET) and IT protocols (REST, MQTT over TLS, Kafka). OT network bandwidth and latency characteristics differ from IT networks, requiring edge processing to reduce data volumes before transmission. Change management in OT environments is more constrained than in IT — firmware updates, configuration changes, and new device connections to OT networks must follow change control processes that maintain production safety and regulatory compliance. These considerations are factored into smart factory architecture from the outset rather than addressed retrospectively.
Perimattic delivers smart factory software through a structured six-stage process that begins before any software architecture decisions are made. Stage one is a free factory operations and automation discovery session: we map the existing automation landscape, IoT devices, production systems, and connectivity infrastructure to understand what data is available, what protocols are in use, and what integration points exist. Stage two produces a detailed system and machine landscape document covering all automation islands, OT network topology, data sources, and target architecture. Stage three designs the smart factory software architecture and builds a proof of concept against one automation system and one enterprise system, validating integration assumptions before full development investment. Stage four is platform development and automation integration: building the smart factory platform, integrating production automation, connecting IoT devices, and deploying AI models into production workflows. Stage five covers testing, validation, and factory acceptance — testing across all integrated systems, validating AI model performance against defined KPIs, and testing edge cases in automation coordination. Stage six is deployment with monitoring, performance tracking, and ongoing support for platform evolution as new systems are added.
Tell us about your factory automation environment — the systems you have, the integration gaps that are limiting the value of your technology investments, and the production intelligence outcomes you are trying to achieve. We will show you exactly how a smart factory software platform can connect your automation, IoT, and AI investments into a coherent production intelligence layer.