Industrial Management Software

We design, enhance, and integrate your expert industrial systems (MES, SCADA, ERP, OEE, Kaptik, DMS, CMMS, and more).

Increase your plant’s performance with industrial management software designed and integrated by KuriosIT.

Our experts support you in the creation, optimization, and modernization of your MES, SCADA, ERP, OEE, CMMS, DMS, and other specialized platforms. We help you connect, automate, and orchestrate your operations through robust, secure industrial solutions that are precisely aligned with your business needs.

Kaptik Smart Factory Platform Industrial SCADA Platforms

Process Automation Custom Software Development

Implementation of Our Smart Factory Software Kaptik

We develop our own smart factory platform. Modular and flexible, Kaptik enables manufacturers to operate more efficiently and proactively by providing real‑time visibility across a wide range of performance indicators.

Real‑time OEE is a starting point—not the destination. Kaptik is a modular smart factory platform that goes well beyond performance measurement. It connects production equipment, centralizes reports, manages operational alerts (Andon), and structures day‑to‑day team execution through the GO‑DMS module, tracking SQCLME indicators (Safety, Quality, Cost, Delivery, Morale, Environment).

Manufacturers can start with the Connectivity module to gain machine visibility, then progressively activate Andon, Visibility, and GO‑DMS modules as operational maturity grows. Each module is customizable to plant realities, and the platform evolves with the organization—no need to replace the solution as needs expand.

Yes. Kaptik connects to all types of production equipment through standard industrial protocols or data acquisition systems. On the management side, it integrates with ERP, CMMS, and other internal tools using its ETL technology, allowing production planning data to be combined with real‑time shop‑floor data.

This connectivity eliminates data silos between the plant floor and management teams : supervisors see live OEE, data flows back into existing systems and deviations automatically trigger alerts or corrective actions based on defined rules. Kaptik adapts to your existing ecosystem—it does not require a full system replacement.

Kaptik is deployed on‑premises, within your own infrastructure. Your production data remains fully owned by you and is accessible at all times—either directly through the data historian or via the platform’s API for reporting and analytics.

This architectural choice is intentional. In manufacturing environments, production data sovereignty is strategic. Local deployment also ensures platform availability regardless of internet connectivity and aligns with segmented IT/OT architectures. For multi‑site organizations, a single Kaptik instance can manage multiple plants connected via an internal VPN.

SCADA Platform Development and Integration

We specialize in the design, development, and deployment of Ignition‑based SCADA platforms, delivering modern, flexible, and scalable supervision solutions tailored to industrial realities.

Analysis of operational requirements and design of centralized or distributed SCADA architectures using Ignition
Development and configuration of Ignition solutions (HMI, alarms, historization, event and recipe management)
Integration of industrial equipment and control systems (PLCs, sensors, standard industrial protocols)
Native Ignition integration with existing IT and industrial systems (MES, ERP, CMMS, data historians, analytics platforms)
Functional testing, commissioning, and startup support (FAT, SAT)
Comprehensive technical documentation and training for operations and maintenance teams

Traditional proprietary SCADA platforms often impose per‑client licensing, rigid architectures, and high upgrade costs. Ignition follows a fundamentally different model: unlimited clients and tags, a web‑native architecture accessible from any workstation or device without local installation, and an open platform built on industrial standards such as OPC UA, MQTT, and SQL.

In practice, this delivers greater flexibility to scale the system alongside operations, native integration with existing IT systems (MES, ERP, CMMS, data historians), and a significantly lower total cost of ownership over time. For industrial environments that evolve—new production lines, new plants, new equipment—Ignition is designed to grow without renegotiating licenses at every step.

One of Ignition’s key strengths is its ability to communicate with most existing industrial equipment through standard protocols such as Modbus TCP/RTU, EtherNet/IP, Profinet, DNP3, OPC UA, and more. In most cases, existing PLCs, sensors, and field equipment can be integrated without hardware replacement.

Projects begin with an analysis of equipment currently in production and their communication protocols, followed by configuration of Ignition drivers and connectivity testing in a pre‑production environment. Functional testing (shop‑floor FAT and on‑site SAT) validates each integration point before go‑live, ensuring that no critical equipment is disrupted during startup. This approach preserves existing investments while modernizing the supervisory layer.

Ignition provides native connectivity to SQL databases, REST APIs, and common enterprise systems, making it a strong foundation for IT/OT convergence. Integration with ERP systems (SAP, Oracle, Microsoft Dynamics), MES, or CMMS enables real‑time production data to flow into management platforms, while parameters or production orders can be sent back to the shop floor.

In practice, this may include automatic OEE data feed into the MES, maintenance work orders triggered in the CMMS by production alarms and synchronization of production recipes between ERP systems and PLCs. Each integration is fully documented and functionally tested prior to production deployment, within an architecture that preserves IT/OT segmentation aligned with IEC 62443 recommendations.

Process Automation with Microsoft 365 and Artificial Intelligence

We design and deploy automation solutions based on Microsoft 365 and artificial intelligence, helping organizations structure, simplify, and secure their business processes while maximizing user adoption.

Analysis of existing processes and identification of high‑value automation opportunities
Design and configuration of automation solutions using Microsoft 365 and Power Platform tools (Power Automate, Power Apps, Power BI, SharePoint, Copilot, etc.)
Development and deployment of AI agents and AI‑driven workflow platforms (Copilot Studio, n8n, Make, etc.)
Creation of digital forms, automated workflows, and validation rules
Integration of M365 solutions and AI agents with existing systems through standard connectors or APIs (ERP, CMMS, CRM, line-of-business applications, external systems)
Implementation of dashboards and reports to monitor processes and performance indicators

The starting point is an analysis of existing processes to identify high‑value automation opportunities: high‑volume repetitive tasks, manual processes prone to error, unstructured approval flows, or data scattered across emails and Excel files. The goal is to prioritize automations that deliver quick, measurable wins, rather than attempting to transform everything at once.

This phase results in a target process map and a phased implementation roadmap. Early solutions—digital forms, approval workflows, Power BI dashboards—also serve as tangible demonstrations to drive team buy‑in. User adoption is designed in from the start, not added at the end.

An AI agent is capable of reasoning, making decisions, and executing actions autonomously based on a defined objective—without human intervention at every step. Unlike a chatbot that simply answers questions, an AI agent can analyze data, trigger workflows, generate documents, update systems, and escalate issues contextually.

In a Microsoft 365 environment, AI agents are deployed through Copilot Studio and integrate natively with Teams, SharePoint, Power Automate, and connected business systems. Examples include an agent that processes incoming requests and routes them to the appropriate department, an agent that monitors Power BI dashboards and proactively alerts managers when thresholds are exceeded or an agent that guides employees through complex processes by asking the right questions at the right time. This is the step beyond rule‑based automation: processes adapt to context, not just predefined rules.

Beyond Microsoft Power Platform, we integrate AI‑driven workflow platforms such as n8n, Make (formerly Integromat), or custom agent architectures built on large language models (LLMs), depending on client needs. These platforms orchestrate complex workflows involving human actions, business rules, and AI reasoning—particularly when multiple heterogeneous systems are involved.

Integration with existing systems (ERP, CMMS, CRM, internal databases) is achieved through standard connectors or REST APIs. Platform selection depends on the context: Microsoft 365 Copilot and Power Automate when the Microsoft ecosystem is dominant and n8n or similar platforms when greater flexibility or multi‑system orchestration is required. All integrations are documented and tested prior to production deployment.

A dashboard or AI agent only delivers value if teams actually use it. Adoption is the blind spot of many automation projects—tools are deployed, but behaviors do not change.

Our approach embeds adoption from the design phase: KPIs and workflows are co‑defined with end users to reflect real daily needs, AI agents are introduced progressively, with clearly defined scopes and appropriate human oversight and training and go‑live support are integral to every deployment. The objective is for each tool to become a natural part of daily work, not an extra technological layer that teams work around.

Custom Software Development and Modernization

We design and evolve custom software solutions, including legacy in‑house applications with accumulated technical debt, into modern web applications that may incorporate artificial intelligence capabilities.

Assessment of existing applications and technical debt to define an appropriate modernization strategy
Design of scalable microservices architectures and development of web applications and backend services, including data and API integration
Modernization of legacy systems into cloud or hybrid environments, with added analytics and AI capabilities
Implementation of software quality, security, deployment best practices, and knowledge transfer to internal teams

Technical debt rarely presents as a sudden failure. Instead, it accumulates gradually until simple changes become time‑consuming, fixes become risky, and the departure of a key developer threatens system continuity. Warning signs include minor changes taking weeks, frequent regressions, reliance on unsupported technologies, dependency on a single individual with critical knowledge, or difficulty integrating with modern tools and platforms.

A structured technical debt assessment allows these risks to be quantified, identifies the most critical components, and defines a realistic modernization strategy. Whether the solution involves phased refactoring, cloud migration, or targeted replacement, the goal is informed decision‑making rather than rebuilding by default.

The decision depends on the business value of the existing software, the condition of its codebase, and the organization’s tolerance for operational risk during transition. Modernization allows valuable business logic accumulated over time to be preserved while replacing outdated technical layers, making it a strong option when the application still fulfills its role but has become difficult to maintain.

Developing a new system is appropriate when technical debt is so severe that modernization would be equally costly, or when business needs have evolved to the point where the existing solution no longer provides a viable foundation. In both cases, modern microservices architectures enable incremental transitions, reducing risk and facilitating knowledge transfer to internal teams.

Integrating AI does not require rebuilding an application from scratch. In most cases, AI capabilities are added through API‑based services such as large language models, predictive analytics, classification, or data extraction, which integrate with existing application logic. This allows AI to enhance specific features like recommendations, anomaly detection, content generation, document analysis, or embedded conversational assistants.

For new applications, AI components are integrated directly into the architecture from the design phase, including data pipelines, inference layers, and model lifecycle management. In all cases, implementation follows established best practices for software quality and security, ensuring sensitive data remains controlled and AI functionality is fully tested and documented.

The transition phase is often the most critical and underestimated part of modernization. A proven approach is to operate the legacy and new systems in parallel for a defined period, migrating users gradually by module or department instead of executing a single large‑scale cutover.

Knowledge preservation begins early with a detailed analysis of the existing solution to capture implicit business rules, edge cases, and logic embedded in the code. This knowledge is formalized and incorporated into the new system before the legacy platform is retired. Knowledge transfer to internal teams, including documentation, training, and access to source code, is built into every delivery to prevent renewed dependency on external providers.

Improve factory performance with tailored solutions in industrial management software

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