Digital Delivery

Deeper insights. Better decisions. Streamlined delivery.

We optimise engineering activities and work management using advanced tools and techniques. We reduce human error through automation, identify hidden vulnerabilities through deeper analysis, and enhance value through better decision-making across the asset lifecycle.

Vulnerability Assessment

See the Full Picture

In complex operating environments, vulnerabilities don't exist in isolation. System performance issues, obsolescence risks, functional safety gaps, control instability, cyber threats, and alarm overloads often share common causes, and sometimes, common solutions. Treating them separately leads to duplicated effort, unnecessary scope, and missed opportunities for smarter, joined-up interventions.

Our vulnerability assessment service delivers a structured, holistic view of automation system risk and performance, across all technologies and lifecycle stages. Whether the objective is risk reduction, cost optimisation, or production assurance, we deliver insight that supports well-informed decision-making.

Independent Assessment

We assess everything, from functional safety, cyber security, and alarm management to process control, system operability, and lifecycle status (hardware and software). We do this with complete independence from original equipment manufacturers or system vendors, offering impartial advice that avoids unnecessary modifications and that solves the right problems.

Our subject matter experts bring decades of field experience across brownfield and late-life assets. Using our proprietary tools, we accelerate issue identification, root cause analysis, and mitigation design. We offer rapid feedback, identify common themes, and propose solutions that balance performance, compliance, and cost.

We tailor the assessment scope and level of detail to suit the requirement, ranging from high-level risk screening to detailed, prioritised mitigation planning with cost estimation. Our data-driven approach supports better decisions and provides a solid foundation for future investment strategies.

Targeted Risk Management

We review whether critical protection systems are still achieving their intended level of risk reduction. This includes validating whether current safeguards and countermeasures remain effective, appropriate, and aligned with changing operational conditions or threat profiles.

Crucially, we do this efficiently. Using a streamlined, evidence-led approach, we confirm whether risks remain tolerable, whether assumptions made during design are still valid, and whether compliance issues exist that could impact safety, security, or integrity. Where gaps are identified, we provide pragmatic, proportionate recommendations, helping you close actions without introducing unnecessary cost or complexity.

Performance Quick Wins

Our assessments target the often-overlooked factors that quietly erode production efficiency and operator effectiveness. Alarm system overload, poorly tuned control loops, inconsistent override management, and missing or outdated operating procedures all contribute to increased operator burden and reduced plant stability. We identify the root causes of these issues quickly, whether they stem from system configuration, process changes, or design legacy.

We identify and implement quick wins as well as long-term improvements: rationalising nuisance alarms, tuning critical loops, flagging underperforming equipment, and assessing operator workload and interface usability. By streamlining operator interactions and stabilising process behaviour, we help restore confidence in the control room, unlock latent performance, and increase uptime, without requiring major rework or capital spend.

OUR APPROACH

Vulnerability Assessment Process

01 Survey

We begin with a site survey, guided by a predefined methodology and workflow. This includes inspection of system panels, identification of critical components, and review of spares availability. System logs, sequence of events files, and historised data are captured for deeper offline analysis.

Beyond data capture, we prioritise engagement with control room operators to understand operational challenges, workload, and system behaviour in real-world conditions. This interaction helps uncover early indicators of risk and performance degradation.

02 Protection Layers

We perform a light-touch functional safety assessment using data gathered during the survey to evaluate the real-world performance of protection layers. We use demand analysis and component availability to build a high-level risk profile.

We validate whether original design assumptions still hold true and highlight any drift between expected and actual performance. This helps identify potential problem areas and prioritise deeper investigation only where it's needed, ensuring any remedial effort aligns with actual risk.

03 Countermeasures

We assess the current cyber security posture by reviewing the availability, integrity, and coverage of key countermeasures. Using the survey data as a baseline, we compare the implemented controls against the management plan and industry good practice to identify potential gaps in protection.

This review highlights basic vulnerabilities such as missing updates, weak access controls, or unsegmented networks. It provides a fast, focused view of where risk may be growing and where cost-effective improvements can be made to protect system integrity.

04 Obsolescence

We perform a review to identify ageing hardware, unsupported software, and discontinued components across all automation systems. We evaluate how these issues impact production risk and identify practical mitigation strategies.

We help clients avoid unplanned production downtime by providing clear visibility of obsolescence risks and their operational impact. Our independent and unbiased assessment supports proactive lifecycle planning, reducing the likelihood of reactive replacements, prolonged downtime, or support unavailability during key operations.

05 System Integrity

We assess the physical and functional state of automation systems to identify signs of degradation, such as hardware condition, communication instability, equipment failures, and declining performance trends. This includes analysing failure records, error logs, and operator observations.

We flag early signs of deterioration before they impact reliability, enabling targeted maintenance, repairs, or upgrades. This helps avoid reactive interventions, protect system availability, and extend the life of critical infrastructure through informed, proactive action.

06 Control Room

We evaluate operator workload, alarm system performance, and system interaction complexity to understand how effectively the control room supports normal operations and abnormal situation management. This includes reviewing alarm rates, HMI layouts, and manual intervention frequency.

We identify where operator loading can be reduced, alarm systems rationalised, and interfaces improved to streamline decision-making and reduce operator fatigue. Our findings help enhance control room performance and enhance operator situational awareness to improve production uptime.

07 Operability

We conduct an operational review of process and utility systems to assess control performance, tuning effectiveness, equipment reliability, and the presence of recurring issues or bad actors.

We identify both quick wins and deeper-rooted problems, flagging where small changes can deliver immediate impact, and where further investigation may unlock significant gains in uptime, efficiency, or reliability. This enables smarter operation, more stable production, and greater efficiency from existing systems.

08 Recommendations

We consolidate findings from across all assessment areas to provide a holistic view of system health and cumulative risk. Rather than treating each issue in isolation, we evaluate how vulnerabilities interact, ensuring recommendations are shaped by the bigger picture, not just individual symptoms.

We deliver prioritised, unbiased recommendations that balance cost, benefit, and operational impact. Our advice is grounded in experience and focused on proportionate, practical actions that deliver measurable value.

Auto Testing

Eliminating Human Error

In automation systems, confidence in logic matters. Yet validating application software against specifications, whether process control or safety related, remains one of the most labour-intensive, and error-prone, activities across the lifecycle. Traditional testing methods depend heavily on manual execution, which is time consuming, expensive, and inherently vulnerable to human error.

At ICSS, we've engineered a smarter way. Using our proprietary tools, we automate the testing of application logic to verify functional compliance, reduce testing time, and increase coverage. Whether validating new builds, supporting brownfield upgrades, or optimising maintenance, our approach removes ambiguity, streamlines execution, and raises the standard of system validation.

Evergreen Specifications

We model application requirements end-to-end, from input conditions and logic to expected outputs, based directly from key specifications. For Safety Instrument Systems, this includes trip conditions, overrides, timers, voting logic, and more, drawn from the SRS, C&E charts, and safeguarding narratives. For process control applications, it includes control narratives, control schemes, and sequence documents. These specifications are translated into a structured model that drives automatic test case generation, ensuring that every intended behaviour is explicitly defined and traceable.

These test cases are then executed in a virtual environment using a backup of the installed application software. By converting the code into an open, standardised format, we simulate real-world scenarios and verify system behaviour without needing to manually test in the factory or on site.

With automated validation in place, specifications are no longer static documents, they become dynamic, testable references that stay aligned with the application throughout its lifecycle and remain evergreen.

Delivery Assurance

Auto testing transforms the way testing is approached in both greenfield and brownfield environments, delivering not just speed, but confidence. By validating application logic early and automatically, we compress the testing window, reduce dependency on physical hardware, and dramatically cut time spent in the factory. Late-stage changes no longer mean schedule risk or excessive manual rework by executed regression testing automatically.

Beyond speed and cost, auto testing improves quality. It achieves far greater test coverage than manual methods, including edge cases, failure modes, and complex logic paths. This reduces the number of defects passed into commissioning and minimises troubleshooting effort at site. The result is tighter delivery, fewer surprises, and systems that are proven and ready to run.

Smarter Proof Testing

Our approach transforms SIF proof testing into a more efficient, lower-risk process. By adopting a partial test methodology, we isolate each subsystem, testing inputs and outputs individually onsite while shifting the logic solver testing offsite into a virtual environment. This eliminates the need for intrusive, full-loop testing during operations, reducing spurious trip risk and avoiding complex overrides.

Combined with automated test generation and execution, the savings compound. Test coverage increases, while effort, downtime, and cost decrease. We integrate this testing strategy into broader functional safety assurance routines, allowing operators to take credit for overlapping tests. It's a practical, standards-aligned approach that improves reliability, supports SIL compliance, and keeps maintenance lean without compromising on integrity.

OUR APPROACH

Auto Testing Process

01 Import

We import all relevant system documentation, C&E charts, safeguarding narratives, control narratives, instrument indexes, alarm and trip schedules, and more, alongside the actual application software. The application logic is converted into a standardised format, ready for simulation and testing.

This step lays the foundation for automated validation. With support for a wide range of formats we eliminate time-consuming manual data entry and ensure that existing deliverables are seamlessly integrated into the modelling process.

02 Model Requirements

We create a structured model that represents the functional requirements and expected behaviour of the system. Using our proprietary component-based framework, we define and configure all elements, from logic functions to hardware, networks, field devices, and communications pathways.

This modelling approach enables complex systems to be represented clearly and accurately, regardless of scale or architecture. By treating everything as a component we simplify configuration, improve traceability, and build a foundation ready for dynamic simulation.

03 Component Linking

We establish the critical link between physical field devices and the software variables that represent them in the application logic. Using structured conventions, regular expressions, and advanced fuzzy matching algorithms, we align tags from the system model with variables extracted from the application code.

This is where automation becomes possible. Our flexible linking methods accommodate inconsistencies in naming, legacy standards, and system-specific quirks, making it fast and reliable to build connections across large systems.

04 Generate Test Cases

We automatically generate test cases that cover the full range of expected system behaviours, positive, negative, and edge cases. Each test follows a structured, repeatable method: set an input condition, execute the logic model, and record the resulting state of each output.

Every test step is derived directly from the requirements and model simulation, ensuring traceability and consistency. Whether validating complex voting logic, interlocks, or control sequences, we generate tests with comprehensive coverage and with no manual intervention.

05 Execute Tests

Test cases are executed against the application logic running in a virtual controller. This simulation environment replicates how the logic would behave in the real system. Where available, tests can also be exported for execution on OEM test harnesses.

This is where the alignment between design and implementation is proven. We validate not just individual functions, but that the system behaves exactly as specified. It's a fast, repeatable, transparent, and reliable way of validating an automation system.

06 Analyse Results

We compare the actual behaviour of the application logic against the expected behaviour defined in the requirements model. For every test case, we analyse the pass/fail results, flag mismatches, and trace them back to either a modelling error or an implementation issue.

By systematically identifying whether failures stem from incorrect requirements modelling or logic implementation, we close the loop on human error. It brings transparency to validation, supports root cause analysis, and ensures that issues are addressed early.

07 Resolve Discrepancies

When discrepancies are identified, we create a copy of the original requirements model and modify this version until all test cases pass against the application logic. Once alignment is achieved, we perform an automatic deep comparison between the updated model and the original.

All changes are transparently highlighted, enabling rapid identification of what was added, removed, or altered. This structured process ensures traceability, preserves the original design intent, and provides clear evidence of whether the issue lay in the specification, the implementation, or both.

08 Findings Report

We produce a comprehensive report that documents the entire validation process. It defines the modelling fidelity, key configuration parameters, and the methodology used to execute the tests.

The report serves as both a validation record and an engineering reference. It includes the final 'as built' specifications with all changes from the original clearly highlighted, providing full traceability and transparency.

AI Adoption

Engineering Reimagined

The emergence of AI marks a fundamental shift in how engineering is done. At ICSS, we're not simply adopting AI at a task level, but rather restructuring our engineering processes to fully realise its full potential. Our AI-enabled engineering platform is built to enhance, not replace, the judgment and expertise of our Subject Matter Experts (SMEs). It automates the repetitive, amplifies insight, and embeds decision-making into the core of every engineering activity.

Our goal is not just to improve engineering productivity. It's about making better decisions, faster, more consistently, and at lower cost. From requirements definition to commissioning and support, our platform combines expert reasoning systems with asset-specific knowledge to reshape how industrial automation is designed, delivered, and supported.

Decision Based Design

Our platform is built around a new approach: decision-based design. Instead of focusing on drawings and deliverables, we focus on the decisions that shape them, capturing intent, reducing uncertainty, and allowing AI to generate the outputs. Every engineering decision is recorded, structured, and traceable. This reduces waste, simplifies assurance, and speeds up delivery across the lifecycle.

SMEs remain firmly in control, but they're now supported by powerful tools that recommend, validate, and optimise. This ensures engineering effort is spent only where it adds value, resolving ambiguity, balancing trade-offs, and responding to change.

Whether we're designing a new control system or resolving an operability issue offshore, the AI platform becomes a co-pilot, guiding, verifying, and accelerating our SMEs while keeping the outcome in focus.

Engineering Reasoning

The core of our platform is a proprietary expert system, built by engineers, for engineers. It captures engineering rules, standards, design patterns, and lessons learned across all industrial automation disciplines from functional safety through process control and commissioning. When combined with powerful off-the-shelf large language model, it becomes a reasoning engine that supports everything from optimisation strategies to automated deliverable generation.

The breakthrough comes when we link this reasoning engine with asset-specific knowledge: documentation, configurations, logs, and system data. This enables the platform to provide targeted insights and recommendations that are not just technically correct, but contextually relevant.

From Advisory to Ownership

We're evolving how engineering services are delivered. Through our AI platform, we can take full ownership of your industrial automation systems, delivering engineering, managing vendors, supporting operations, and driving performance across the asset lifecycle.

This end-to-end service model simplifies stakeholder engagement, accelerates delivery, and provides a single point of accountability for everything from minor modifications to long-term asset strategy.

As the platform evolves, so does the service model, with a long-term goal of enabling fixed-price, fixed-scope engineering that delivers improved outcomes at reduced cost.

OUR APPROACH

AI Adoption Process

01 Assess & Benchmark

We begin by conducting a structured assessment of the current automation systems, engineering processes, and operational support models. We also benchmark current delivery methods, tools, and documentation practices.

This step provides a clear, data-driven baseline of where value is being lost, and where AI can deliver the biggest impact. It ensures adoption efforts are grounded in real system needs and helps prioritise areas for quick wins and long-term transformation.

02 Adoption Plan

We work with stakeholders to define specific, measurable goals for the adoption of AI-enabled engineering. Objectives might include improving engineering throughput, reducing vendor costs, accelerating commissioning, streamlining compliance, or enhancing operational support.

Each plan is shaped by asset-specific priorities and constraints. By aligning platform capabilities with your business priorities, we ensure a fit-for-purpose strategy that delivers meaningful outcomes and is achievable within existing constraints.

03 Asset Knowledge

We structure and import critical engineering and operational knowledge into the AI platform. This includes design philosophies, standards, drawings, specifications, logs, alarm databases, system configurations, and historical data, everything needed to make informed engineering decisions.

AI is only as smart as the knowledge it's built on. By creating a tailored knowledge graph for each asset, we enable the platform to offer relevant, accurate insights and automate decisions with full context, cutting through noise and eliminating duplication of effort.

04 Reasoning Systems

We activate our library of expert systems, which are modular AI tools configured by our SMEs that encode engineering logic, standards, and best practice across all major automation disciplines. These are adapted for the specific asset and engineering process using custom rule sets.

This is where AI begins to reason like an engineer. Whether checking compliance, generating deliverables, or reviewing third-party designs, the platform becomes a trusted co-pilot, supporting SMEs and accelerating workflows with consistent, context-aware intelligence.

05 Engineering Processes

We shift engineering workflows from a deliverable-centric approach to a decision-based design model. Key decisions are captured, structured, and linked to design intent, allowing the AI to automate documentation and reduce iteration.

This transformation reduces engineering effort and makes it traceable, scalable, and repeatable. It enables engineering to move faster without compromising quality, freeing SMEs to focus on judgement, strategy, and complex trade-offs.

06 System Modelling

We create digital models of key automation systems, such as SIS, PCS, and F&G, using our platform's modelling tools or by importing existing software and hardware configurations. These models serve as the foundation for specification generation and auto testing.

This step enables detailed models that replicate real-world behaviour. These models form the basis of consistent specifications, smarter testing, and further enhance the AI to provide greater insight and offer even more novel solutions to complex problems.

07 Application Generation

We extend the platform to automatically generate application logic and test cases directly from design decisions, dramatically reducing the effort needed to model detailed requirements. The platform incorporates vendor-specific quality manuals, best practices, and historical lessons learned to generate not just correct, but supportable and maintainable, applications.

This step represents a leap in engineering productivity. Applications are not only delivered faster, but with higher quality, consistency, and traceability. Testing is embedded from day one, and every application is automatically validated.

08 Learn & Evolve

We embed the platform into day-to-day support and maintenance processes, enabling fast access to asset knowledge, smarter troubleshooting, and AI-assisted optimisation. System models and knowledge graphs evolve over time with use and feedback.

AI adoption doesn't stop at go-live. We create a sustainable capability that matures with your operations, improving system reliability, supporting operator performance, and driving continuous improvement in how engineering value is delivered.

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Need help with digital delivery, automation, or AI adoption?

Tell us what you are trying to understand, automate, or transform across greenfield projects, brownfield upgrades, or late-life assets. We will help shape the scope, de-risk the decision, and define a practical route to better delivery.

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