Assets, orders and plant knowledge on a single, real-time data model. One platform for everyone responsible for production: from the shift leader to the CEO and COO, from the pilot plant to the global standard. Productive in hours.
An MES platform has to do three things: connect data, contextualize it in real time, and surface it in applications for every role. Fragmented data models are common in plant IT landscapes: machine data in one system, order data in another, reports built on a third source. The result is friction at the seams and duplicated upkeep.
The SYMESTIC platform brings all three functions together in a single architecture: three layers (Connect, Contextualize, Act) on one shared data model. The symestic AI layer spans all three. What the platform does is described in this section. Where and how it runs is shown in Section 6: Architecture.
→ The technical architecture in detail: Section 6: Architecture
Assets, ERP, third-party systems, people and production know-how, standardized in one data model.
Go to Layer 1Real-time contextualization. One data model across every plant.
Go to Layer 2Three app families plus an AI layer, all built on the same platform logic.
Go to Layer 3Machine data shows what is happening. Only in the context of order, material, shift, quality and experience does it become clear why. OEE without an order context isn't meaningful enough: a machine can run at 95% OEE and still produce the wrong product at the wrong time. Third-party systems without integration become data islands: a released QMS batch or available WMS stock stays invisible to production. Root causes without captured experience are reconstructed from scratch again and again. The SYMESTIC platform connects five domains in a standardized way within one shared data model.
Machines, lines, robots, process equipment, sensors. From the brownfield asset with no communication interface to the connected, modern robot cell.
Orders, bills of materials, material data. Bidirectional: orders flow in, feedback flows back.
Proven REST API integration, no rip-and-replace. Integrated bidirectionally: the platform builds on the IT landscape you already have.
From the operator on the shop floor to the CEO and COO. Operators, shift leaders, supervisors, engineers, plant managers, COOs and CEOs. Every role gets the view it needs.
Captured in a structured way. Roadmap: semantically searchable via symestic AI from Q4 2026.
Connected data only becomes valuable once it knows its context. AI capabilities without a clean data model stay demos and never reach production. The Contextualize layer brings the five domains from Connect together in a unified data model: contextualized in seconds, identical across every plant, available to every app and every AI agent. The data model is based on the ISA-95 standard, refined over 25 years of MES practice.
Structured to the ISA-95 standard and refined over 25 years of MES practice. Order, machine, shift, material, quality, personnel: every entity and its relationships are defined in the model before the first app is built on top of it.
Every data point is linked to its order, machine, shift and material within seconds. Signals become information.
Identical data model across every plant. KPIs are directly comparable, with no after-the-fact harmonization. In productive use across 143 locations in 20 countries today.
Clean data flows are the prerequisite for productive AI. Today the DATA‑HUB carries three productive AI functions: translation of downtime reasons, AI RegEx configuration, and summarization of shift logs. Roadmap: from Q4 2026 an additional MCP server connecting external AI platforms such as Claude, ChatGPT, Gemini and Microsoft Copilot.
Apps are what operators, shift leaders and plant managers actually see, the last mile between platform and production. All three SYMESTIC app families run on the same data model: connect an asset once, integrate an ERP once, configure an operator input once, and every app benefits immediately. Future apps benefit too, with no fresh IT project. One data source, one logic, one way of working across every app, on a data model that grows with every new module.
Measure efficiency, locate losses, quantify improvements: the view from above, across shifts, lines and plants.
Control orders, guide operators, minimize downtime: the daily rhythm on the shop floor, without Excel islands and duplicated upkeep.
Prevent downtime, raise asset availability, catch deviations early, before they turn into scrap or complaints.
Cloud architecture is the right answer to scaling, AI integration and multi-site consolidation. It is not the right answer to an internet outage during the three-in-the-morning shift. The SYMESTIC platform solves both: cloud-native on Microsoft Azure, edge-resilient locally at the plant, with autonomous continued operation during a cloud outage.
Microsoft Azure for hosting, scaling and security from one source. Multi-tenant architecture with strict per-customer data separation. Cyber security is anchored in the platform kernel.
Edge components for fast, secure connections per asset, line/process or plant (self-service).
The right access for every role to the Manufacturing Platform — from the operator on the shop floor to the CEO. All draw on the same unified data model: no duplicate truths, no synchronization.
The cloud layer carries the platform logic: the unified data model, all apps and reports, the AI layer and the MCP integration. Microsoft Azure as the platform.
Software component for OPC-UA acquisition at modern assets. Preprocessing right at the source. Local buffering against transmission disruptions. Runs on the IoT Box or a customer VM. Cloud-configurable.
Hardware for connecting assets via digital signals. Universally connectable, from the 1995 controller to the 2024 machine. Optionally with cellular for sites without a stable LAN connection. Scalable to several thousand units per plant.
The cloud-edge-native successor to our proven on-premises traceability engine, fully integrated into the platform. Process interlocks along the value chain. Local decision-making for safety- and quality-critical process steps. Autonomous operation for up to a full production day during a cloud outage.
Five ways into the platform for every role and every situation: Web App, Mobile App, plant display, API + MCP, shopfloor clients. → Detailed view in Section 7: Access
Three layers, one data model. What is captured, buffered and controlled at the edge flows into the same unified data model as everything else. Apps, AI agents and reports see the edge data seamlessly, with no separate integration and no data breaks between the edge world and the cloud world. This is the structural prerequisite for the functional logic from Section 2: Connect / Contextualize / Act.
One platform, five ways in. Every stakeholder gets the view that fits their role, on the same data model.
AI in production promises enormous leverage: decision support, gradual automation, anomaly detection. Yet much of what the market announces as AI capability runs today as a demo on curated datasets or as a coming-soon entry on a roadmap with no firm date. The SYMESTIC platform draws a consistent line between what ships in production and what's planned on the roadmap. Productive today: three concrete AI functions: translation of downtime reasons, AI-assisted RegEx configuration, and summarization of shift-log entries. Roadmap: a step-by-step extension with clear quarterly targets. Every stage ships enterprise-ready, not just announced.
Three concrete AI functions: named, and shipping today. No "AI Assistant" promises, but targeted AI functions at specific points.
Translates downtime reasons into every language the platform supports: operators document in their native language, other roles see the translation.
For device connections, symestic AI builds the required data patterns from natural language, with no knowledge of RegEx syntax.
Condenses shift-log entries down to what the next shift needs to know. The audit trail stays untouched.
Five stages. Visually distinguished between AI-direct (purple) and AI-prerequisite (gray-blue). Every stage ships enterprise-ready before the next one arrives.
Downtime-reason translation, AI RegEx for device configuration, shift-log summary.
Granular permissions and audit log: the platform prerequisite for secure AI adoption in regulated industries.
MCP Server (Read) with OEE queries, downtime lists and order status. Compatible with Claude, ChatGPT, Gemini and Microsoft 365 Copilot. Plus a Reporting Agent.
Expanding the data foundation for the next AI stages: Connected Order and Material Flow, Part Traceability.
Anomaly Detector with pattern recognition on process data. Limited MCP Write Actions with human-in-the-loop approval. Workflow Agent launches.
How we govern AI responsibly. Three anchors, all AI-specific, not platform properties.
Every AI response points to the underlying platform data. AI is traceable, not a black box.
Writing AI actions require human confirmation. The human decides, AI assists.
A deliberate sequence: read access for external AI platforms first (Q4 2026), then write access with approval (Q2 2027). No open write rights without an audit chain.
Data sovereignty, encryption, German data centers, ISO 9001 and GDPR compliance are platform properties. See Section 9: Enterprise & Compliance →
Industrial companies don't choose their MES platform on OEE and ROI alone. They also choose it on security, auditability and regulatory fit. CISOs, compliance leads and supplier auditors come into the selection process early, and ask hard questions. The SYMESTIC platform gives concrete answers: in the architecture, in operations and on the roadmap.
The platform was rebuilt from the ground up in 2019, with cyber security as an architectural property, not a retrofit.
We support NIS2 obligations through documented platform governance: audit trails, access control, process documentation.
For customers with heightened data-residency requirements, German Azure regions are available on request.
Anyone who starts with SYMESTIC scales on their own: more assets, more lines, more plants, without a consulting project per site. Three examples from practice.
From pilot to group-wide solution in half a year (DE, CZ, HU), independently by the customer team.
Replacement of an existing MES solution in 6 months, full connection within half a year.
GMP pharma, scaled in 3 weeks. Proof that the platform also carries validation requirements.
Prove the value yourself, or assess the benefit for your own plant with an MES expert. Both paths are pressure-free and with no contractual commitment.
Full access to the platform. No IT effort, no contractual commitment. Start right away with your first asset.
30 to 45 minutes with our MES expert, your use cases, and a first assessment of the benefit. No obligation.
Common questions, direct answers. For deeper topics we point to the architecture whitepaper.
