IQ integrates AI across digital engineering, digital delivery, information management and asset management to transform data into actionable insight. Our approach is practical, engineering-led and focused on delivering measurable outcomes across asset-intensive environments.
By embedding AI within established workflows, IQ enhances data quality, accelerates delivery and improves decision-making—ensuring that information is not only available, but reliable, structured and usable.
“Any sufficiently advanced technology is indistinguishable from magic.”
Arthur C. Clarke
Agentic AI Models
IQ leverages agentic AI models to automate and orchestrate complex, multi-step workflows across digital engineering, document control and information management processes. These models operate with defined rules, context awareness and validation logic, enabling consistent execution of tasks traditionally performed manually.
• Reduces manual effort and delivery time
• Improves consistency and eliminates human error
• Enables scalable service delivery
Engineering & Legacy Data Extraction & Restructuring
IQ applies AI-driven techniques to extract, interpret and restructure engineering and operational data from unstructured sources including drawings, PDFs and legacy systems. This data is transformed into structured, governed formats aligned to client data models and business requirements.
• Unlocks high-value data trapped in drawings and PDFs
• Accelerates digital transformation
• Reduces cost and effort of data migration
Digital Delivery & Information Management Visibility
IQ enhances visibility across the data lifecycle by applying AI to monitor data completeness, quality and status across engineering and operational systems. This creates a transparent, real-time view of information maturity and readiness.
• Improves stakeholder confidence
• Reduces rework and project risk
• Enables better decision-making
Data Quality Automation
IQ deploys AI to continuously validate, cleanse and monitor engineering and asset data, identifying anomalies, inconsistencies and gaps in real time. These processes are embedded within digital delivery workflows to ensure ongoing data integrity.
• Ensures reliable and compliant data
• Reduces downstream operational risk
• Improves regulatory alignment
Causal AI for Predictive Maintenance
IQ applies causal modelling techniques to identify the true drivers of equipment failure and performance degradation, moving beyond traditional correlation-based analytics. This enables more accurate prediction of failure modes and maintenance requirements.
• Improves maintenance accuracy
• Reduces downtime / increase productivity
• Enhances safety outcomes
Legacy Data Restructuring for Decision-Making
IQ transforms fragmented and inconsistent datasets into structured, decision-ready information aligned to asset hierarchies, maintenance systems and operational workflows. This enables meaningful analysis and informed decision-making across the asset lifecycle.
• Converts data into decision-ready formats
• Improves planning and forecasting
• Supports investment decisions
Operational Optimisation
IQ applies AI-driven analysis across production, maintenance and asset data to identify inefficiencies, bottlenecks and system constraints. These insights support continuous improvement and optimisation of plant performance.
• Improves production efficiency
• Reduces operating costs
• Identifies system constraints
Decision Support & Scenario Modelling
IQ enables rapid evaluation of operational and capital scenarios by integrating structured data, engineering inputs and AI-driven insights. This supports more informed and timely decision-making across projects and operations.
• Enables faster, evidence-based decisions
• Reduces capital and operational risk
• Aligns engineering and operations
