Everything you need
to know.

Yes. IO is designed to amplify operations teams with always-available industrial reasoning. It helps monitor situations, connect signals, identify issues earlier, and support daily decisions with clear recommendations.

Where policies allow it, IO can also execute specific delegated actions. The goal is not to replace operational teams, but to give them more reach, more consistency, and faster access to expert-level support.

A first IO mission can usually be deployed in weeks, not quarters. The right starting point is a bounded operational problem with available data, clear levers, and a measurable success metric.

In many cases, the first concrete results can be visible within 8 to 12 weeks. Depending on the mission, these results may relate to performance, quality, planning, energy use, operational discipline, or reduced expert workload.

Yes. IO is built to sit above existing plant and enterprise systems. It connects to what is already there: SCADA, historians, PLC data, ERP, CMMS, MES, asset systems, documents, and other operational sources.

There is no rip-and-replace. IO creates the context layer needed for AI to reason across different systems, while your existing logic, equipment, and operational structure remain in place.

IO is designed around control, explanation, and traceability. In supervised mode, IO recommends and explains, but the operator decides. The user can review the reasoning, the evidence, and the proposed action before anything is executed.

Delegated execution is only enabled for specific, approved actions under explicit policies. It should start with bounded, well-tested cases where the value and risk are understood. Every recommendation, approval, and action is recorded in the audit trail.

Yes. IO is designed to support both modes.

In supervised mode, IO analyses, recommends, and explains while the human remains responsible for confirmation. As confidence grows, selected actions can move into delegated execution, always under explicit operational policies.

Delegation can be scoped by mission, asset, command type, operating condition, range, or approval rule. Teams can increase or reduce delegation over time as experience grows.

Yes. IO can use structured and unstructured information as part of a mission: plant documentation, manuals, drawings, asset files, reports, procedures, images, voice notes, and even 3D models when useful.

The important point is to define this during the mission design. IO should know which sources matter, when to use them, and how they relate to the plant context, signals, assets, and operational decisions.

Because the economics of industrial operations increasingly depend on more accurate decisions, at increasing frequency, that are contextual and cross-system — well beyond what traditional dashboards handle alone. IO gives teams governed AI that operates on top of existing systems, with proof of value before scale and a pricing model built for enterprise adoption.

Cleverdist publicly highlights deployments in real industrial environments: Naturgy's centralised operations across 10 combined-cycle power plants and 17 gas turbine units, and CERN's CMS environment with 10M+ I/O parameters. These references show the platform is designed well beyond demo-scale use cases.

ROI is proven mission by mission. IO starts with a narrow business outcome — throughput, quality, downtime, energy or planning performance — and a measurable baseline. The first proof of value is positioned in 8 to 12 weeks, so sponsors can validate impact before committing to broader rollout.

IO is designed to evolve at your pace. Start with AI recommendations and human validation, then delegate only the actions that have proven safe, valuable and controllable. The model is not all or nothing — it is autonomy at your pace, under your policies.

IO is built for industrial operations, not generic task automation.

It combines deep industrial integration with Cleverdist's R&D on Autonomous Reasoning Systems, developed through our collaboration with CERN. This gives IO the capability to run long-horizon operational missions with governed delegation and full traceability.

The strongest IO implementations share five success factors: one high-value pain point, a measurable KPI, access to the right systems and experts, clear governance from day one, and an executive sponsor who keeps the team focused on business value rather than experimentation for its own sake.

Onboarding depends on mission scope, system access, data readiness and governance needs. The commercial principle is to keep the first mission controlled: dedicated onboarding, clear success metric, limited scope, and expansion only after value has been demonstrated. Pricing is shared on request.

IO Edge runs on a customer-provided machine close to the operational environment. It handles connectors, local analytics and the local user interface.

IO Cloud is a dedicated customer instance in Cleverdist's Azure environment for agent orchestration, mission lifecycle management and AI inference access.

By default, IO Cloud is a dedicated private instance for the customer within Cleverdist's Azure subscription. For stricter requirements, the target cloud architecture, region and ownership model can be discussed during onboarding.

Operational processing happens locally on the Edge. IO Cloud receives only the selected contextual information required for AI inference and industrial reasoning, such as summaries, calculated values, relevant alarms, KPIs or mission context. Raw telemetry, continuous historian data and full data histories are not sent off-site.

IO runs on Microsoft Azure services with a broad compliance portfolio, including ISO 27001, ISO 27017, ISO 27018 and SOC 1/2/3 reports, with regional references such as ENS where applicable. IO is also CRA-ready, and Cleverdist can provide a project-specific cybersecurity assurance package covering architecture, security boundaries, access control, vulnerability handling, updates, audit trails and customer responsibilities. For NIS2-regulated customers, Cleverdist can provide a dedicated support package to help document IO within the customer's cybersecurity governance and supplier assurance process.

Yes, IO can connect through standard APIs or integration layers to ERP, CMMS, EAM, APM, planning systems and more. Onboarding specifies read/write permissions, authentication, audit expectations and whether actions are human-approved or delegated — all parameters are auditable and reversible.

The key resource is a knowledgeable process or operations expert who can explain how the target process works, what decisions need support, and what success should look like. IT/OT support is required for system connectivity, access and deployment topics. A business or operational sponsor can help prioritise the mission and validate the value. A first IO mission can typically be onboarded and validated within 8 to 12 weeks.

At the end of a pilot, project-specific data is handled according to the agreed closure process and Cleverdist's standard data-handling practices. Data held by Cleverdist or IO Cloud can be deleted or returned as agreed with the customer. Where required, Cleverdist can provide written confirmation that the agreed clean-up has been completed.

Most AI solutions chain tasks or automate isolated workflows. IO is an Autonomous Reasoning System built for industrial environments: it models how experts reason across context, signals, constraints and policies. That makes it suitable for missions requiring long time horizons and continuous decision support.

Pick a mission where the pain is frequent, expensive and decision-heavy: repeated troubleshooting, quality drift, production bottlenecks, maintenance prioritisation, energy optimisation or plan repair. The best first mission has available data, engaged experts and a KPI that stakeholders already care about.

No. IO can start from the operational reasoning, system context and data that matter for the mission. Clean historical data helps, but the first goal is not to build a perfect data lake - it is to connect the minimum useful context and prove a valuable decision loop.

That is exactly the recommended model. IO starts on one mission, one site, with a clear success indicator. Once value has been proven, you add missions and expose more data on the same architecture.

IO is built around explicit policies that define exactly what actions are authorised within each mission. These policies are the foundation of auditability: every action IO takes or recommends is traceable back to a policy your team has approved and can revise at any time.

IO integrates a complete audit trail for every recommendation and action. In Delegation mode, policies are explicit, scoped and reversible. You always know why IO recommended what, in what context, and which rule applied - designed for your internal teams, validation processes and industrial documentation requirements.

IO adapts to your team's way of working. From day one, it learns how your teams operate, which decisions matter, what actions are preferred in typical situations, and which practical know-how is not always documented. Adoption happens through real missions, operator feedback and progressive alignment with the customer's operating culture.