Manufacturing and Industrials
Enterprise AI for Manufacturing and Industrials
Digiton designs, builds, and governs production AI agents for manufacturers and industrial operators: predictive maintenance, quality inspection, production scheduling, supply coordination, and engineering knowledge capture with RAG. EU-native engineering, integrated with your existing MES, ERP, and shop-floor systems.
The problem is not a lack of AI demos. It is production reliability.
Most industrial AI pilots stall before they reach the shop floor. The model works in a notebook, then collides with reality: a fifteen-year-old MES, an ERP with custom modules, sensor data in three incompatible formats, and an operations team that (rightly) will not trust a black box near a production line. The gap between a promising proof of concept and a system that runs every shift, every day, is where most value is lost.
Digiton works the other side of that gap. We are an EU-native engineering partner, Lisbon-based, focused on building production AI agents and workflow automation that integrate with the systems you already run and meet the governance bar a serious manufacturer requires. Our work sits inside the enterprise AI agency practice and is delivered with the same reliability, security, and oversight expectations you apply to any critical operational system.
Where AI creates measurable value in industrial operations
We focus on a small number of high-leverage use cases where the ROI is defensible and the integration path is clear. The figures below are industry ranges reported by vendors and analysts, not guarantees; your baseline determines the realistic target.
| Use case | What the agent does | Reported industry outcome (hedged) |
|---|---|---|
| Predictive maintenance | Monitors sensor and equipment data, flags degradation early, schedules intervention before failure | Vendor and analyst sources commonly cite 20-50% reductions in unplanned downtime; Siemens estimated unplanned outages cost large enterprises roughly 11% of revenue in its 2024 study |
| Quality inspection | AI vision and anomaly detection on the line, surfaces defects, routes exceptions to humans | Higher first-pass yield and earlier scrap detection (results vary by process and defect type) |
| Production scheduling | Optimizes sequencing and changeovers against constraints, demand, and material availability | Reduced changeover loss and improved on-time delivery |
| Supply coordination | Agents reconcile orders, lead times, and exceptions across suppliers and ERP | Faster exception handling and fewer manual reconciliations |
| Knowledge capture (RAG) | Retrieval over manuals, SOPs, maintenance logs, and engineering docs for instant, cited answers | Faster troubleshooting and preserved tribal knowledge as experienced staff retire |
Knowledge capture: RAG over your technical documentation
Industrial knowledge is trapped in PDFs, maintenance logs, CAD annotations, and the heads of senior engineers. As that workforce retires, the cost of a slow answer rises. We build retrieval-augmented generation (RAG) systems over your controlled documentation so a technician or planner gets a precise, cited answer in seconds, grounded in your actual manuals and procedures rather than a generic model guess. Every answer links back to its source document, which matters for both trust and audit. Done well, RAG is the lowest-risk, fastest-payback entry point for a first production deployment.
Integration with MES, ERP, and shop-floor reality
An agent that cannot read from your MES or write back to your ERP is a science project. Our delivery starts with the integration map, not the model. We connect to SAP, common MES platforms, historians, and SCADA data through their supported interfaces, respect existing data ownership and access controls, and design for the messy parts: intermittent connectivity, legacy formats, and the fact that the line cannot stop for an upgrade. Where real-time control is involved, the AI advises and humans decide; we do not put a probabilistic model in a safety-critical control loop.
Governance, security, and the EU AI Act
For a CTO, COO, or Head of Data, governance is the deciding factor, not a footnote. Industrial AI touches safety, employees, and supply chains, so it has to be auditable and controllable. We treat this as core engineering. Our standing reference is our AI agent governance framework, which covers access control, audit logging, human-in-the-loop checkpoints, evaluation, and rollback.
Security includes defending the agents themselves. As autonomous agents gain access to operational systems, prompt-injection and tool-abuse attacks become a real attack surface; see our work on agentjacking defense for how we harden agent permissions and tool use. On the regulatory side, the EU AI Act applies to certain AI components in machinery and to safety functions. Per the Digital Omnibus provisional agreement of May 2026, the main high-risk obligation deadline for Annex III systems was moved to December 2027, and several machinery-related clarifications were issued; we track this actively so your deployment is designed to fit the obligations rather than retrofitted later. As an EU-based engineering partner, GDPR and EU AI Act readiness are built into how we work, which is a trust asset when your data and operations are in scope.
How a Digiton engagement runs
- Enterprise AI audit. We map your systems, data, and the two or three use cases with the strongest, most defensible ROI.
- Architecture and governance design. Integration plan, security model, oversight checkpoints, and success metrics agreed up front.
- Build and integrate. Senior engineers build the agents and connect them to your MES, ERP, and document stores.
- Controlled rollout. Staged deployment with monitoring, evaluation, and human oversight before any scale-up.
- Operate and improve. Ongoing measurement, change management, and tuning against the agreed metrics.
For broader context on adoption patterns and where operations leaders are placing their bets, our state of AI operations report covers the data we see across engagements.
If you are evaluating production AI for a plant, a network of sites, or a group function, the most useful next step is a scoped assessment of where it pays. Book an enterprise AI audit and we will map the highest-value, lowest-risk path for your operation.
Frequently asked questions
How is this different from a generic AI consultancy?
We are an engineering partner, not a slide deck. Digiton builds and operates production AI agents that integrate with your MES and ERP, with governance, security, and human oversight engineered in. The deliverable is a system that runs every shift and measurably, not a strategy document and a pilot that never reaches the floor.
What size of engagement do you take on?
We work with large organisations on engagements that justify senior engineering, typically starting from five-figure euro scopes and scaling to multi-site programmes. Our focus is enterprises buying serious, governed AI deployment, not low-cost point tools. Engagements begin with an audit that defines the value and the integration path before any build.
Will AI touch our safety-critical control systems?
No. Where real-time or safety-critical control is involved, our agents advise and humans decide. We do not place a probabilistic model inside a safety control loop. This is both sound engineering and aligned with how the EU AI Act treats safety components in machinery, where failure could endanger health or safety.
How do you handle our legacy MES and ERP?
We start with the integration map, not the model. We connect through supported interfaces to SAP, common MES platforms, historians, and SCADA data, respecting existing access controls and data ownership. We design for intermittent connectivity, legacy formats, and the constraint that production cannot stop for an upgrade.
What is the fastest use case to get into production?
Usually RAG-based knowledge capture over your technical documentation. It carries low operational risk because the agent retrieves and cites your own manuals and procedures, it pays back quickly through faster troubleshooting, and it preserves expertise as experienced staff retire. It is a strong first deployment that builds organisational trust for later work.
Can you quantify the ROI before we commit?
The audit produces a defensible business case tied to your baseline: current downtime cost, scrap rate, planning effort, or reconciliation hours. Industry sources report ranges such as 20-50% reductions in unplanned downtime, but we model your numbers rather than borrow vendor averages. You see the expected return and the assumptions behind it before any build starts.
How do you address the EU AI Act for industrial AI?
We track it actively. The Digital Omnibus provisional agreement of May 2026 moved the main high-risk deadline for Annex III systems to December 2027 and clarified several machinery-related points. We design deployments to fit the applicable obligations, including documentation, logging, and oversight, so compliance is built in rather than retrofitted under deadline pressure.
Why does an EU-based partner matter for us?
If your data, employees, or operations fall under GDPR or the EU AI Act, an EU-native partner is a trust asset. Senior engineering in Lisbon means GDPR and EU AI Act readiness are part of how we build, not an afterthought. For buyers in regulated or data-sensitive industries, that reduces legal and reputational risk on a serious deployment.
How do you secure the AI agents themselves?
Autonomous agents with access to operational systems are an attack surface. We harden agent permissions, constrain tool use, and defend against prompt injection and agent hijacking, covered in our agentjacking defense work. Combined with audit logging and least-privilege access, this keeps the agents controllable and the blast radius small if something is probed or misused.
What does change management look like on the shop floor?
Operators have to trust the system or they will route around it. We design for that: cited answers, transparent reasoning, human checkpoints, and a staged rollout with monitoring before scale-up. We involve operations and maintenance teams early so the agents fit existing workflows rather than fighting them, which is usually the difference between adoption and shelfware.
Can you work across multiple sites or a group function?
Yes. We design for multi-site deployment with shared governance, consistent security, and per-site configuration. A common pattern is to prove value at one plant under tight measurement, then template the architecture and governance for the wider network so each rollout is faster and lower risk than the last.
Do you build or do you also operate the systems?
Both. We build the agents and integrations, then offer ongoing operation: monitoring, evaluation, tuning, and change management against the metrics agreed at the start. Production AI is not set-and-forget; models drift, data shifts, and processes change. Continuous measurement and senior engineering attention are what keep the ROI real over time.
What is the first step to engage Digiton?
Book an enterprise AI audit. We map your systems, data, and the two or three use cases with the strongest and most defensible ROI, then return a prioritised plan with an integration and governance design. It is a low-commitment way to get a clear, credible view of where production AI pays for your operation.
Related
Ready to put AI to work?
Book a discovery audit and we will map the highest-ROI AI agents and automations for your business.
Book a discovery audit →