Use Cases
Intelligence where it creates value.
Give partners an execution layer for their AI solutions.
Technology and industry partners want to create AI solutions without having to build and operate the entire underlying AI stack. Runtime provides the governed execution layer on which partners integrate workflows, models and domain expertise. Value can be assessed through time to market, delivery effort, deployment repeatability and operating cost.
Give partners an existing execution layer on which to build and deploy their AI proposition.
Provide a governed underlying environment rather than requiring each solution to recreate the AI stack.
Combine partner workflows, models and domain expertise into propositions that can be deployed repeatedly.
Deploy enterprise AI within required controls.
Sensitive data, governance and audit requirements can constrain how organisations deploy AI. Runtime provides a controlled environment for models, agents and workflows within the required customer or sovereign infrastructure. Value can be assessed through deployment time, governance compliance, auditability and operational predictability.
Run intelligence within the required customer or sovereign infrastructure.
Apply controlled execution and governance to models, agents and workflows.
Provide greater visibility into how enterprise AI operates and changes over time.
Extend monitoring across distributed infrastructure.
Manual inspection cannot scale across large distributed infrastructure networks. Runtime processes camera, vehicle, sensor or drone data locally and can classify issues and orchestrate the next action. Value can be assessed through inspection coverage, response time, field resource utilisation and earlier issue identification.
Process operational data locally to detect and classify issues closer to where they occur.
Use contextual information to determine which issues require attention first.
Direct field resources towards identified issues rather than relying solely on routine manual inspection.
Reduce manual processing across document-intensive work.
Manual intake, repeated data entry and disconnected evidence handling increase processing cost and cycle time. Runtime combines conversational intake, document and image understanding, governed AI agents and existing enterprise systems. Value can be assessed through processing time, manual effort, completeness of information and workflow throughput.
Capture and structure information earlier through conversational, document and image-based workflows.
Reduce repeated data entry and handling across document-intensive processes.
Bring evidence and enterprise information together so cases can be assessed more quickly and completely.
Scale customer operations without losing control.
Rising service volumes, inconsistent interactions and data residency requirements can make it difficult to scale customer operations safely. Runtime orchestrates secure voice and AI workflows across existing customer and enterprise systems. Value can be assessed through service capacity, response time, consistency and the proportion of work handled without manual intervention.
Use AI workflows to handle higher volumes of customer interactions consistently.
Coordinate voice and enterprise systems to support faster and more consistent service.
Reduce repetitive operational workload so frontline teams can focus on interactions requiring human judgement.
Turn site-level intelligence into a portfolio view.
Owners and operators can struggle to compare performance consistently across buildings, sites and asset classes. Atlas aggregates local intelligence into a portfolio view for benchmarking, prioritisation and cross-site learning. Value can be measured through portfolio operating cost, capital efficiency, asset performance and revenue opportunity.
Compare operational performance more consistently across sites, buildings and assets.
Identify where capital and improvement activity can be directed towards higher-value opportunities.
Identify recurring patterns and improvement opportunities across the wider portfolio.
See building performance in context.
Building data is often held across multiple systems, leaving facilities teams to respond after cost, energy or service issues are already visible. Atlas creates a connected view of building performance, maintenance, occupancy and operational workflow. Value can be measured through energy intensity, maintenance cost, response time, occupancy and service performance.
Connect operational information to identify inefficiencies and opportunities to improve building performance.
Give facilities teams better context to identify issues and respond more effectively.
Bring together maintenance, occupancy and performance information to support better use of building resources.
Move from reactive maintenance to earlier intervention.
Reactive maintenance and limited performance visibility can create avoidable downtime, disruption and underused assets. Atlas identifies performance patterns, anomalies and maintenance priorities across equipment and operations.
Identify performance patterns and opportunities to make more productive use of operational assets.
Surface anomalies and maintenance priorities earlier to reduce avoidable disruption.
Help teams direct maintenance resources towards the equipment and issues that require attention most.
See project risk earlier.
Project information is often dispersed across site, design, planning and commercial teams, making emerging risk harder to see and act on. Atlas connects relevant project and operational signals to surface dependencies, risk and performance issues earlier.
Identify emerging issues earlier so teams can intervene before they create avoidable rework.
Bring project and operational signals together to support faster decisions across teams.
Surface risks and dependencies earlier to help reduce delays and improve resource utilisation.
Scale customer operations without losing control.
Rising service volumes, inconsistent interactions and data residency requirements can make it difficult to scale customer operations safely. Runtime orchestrates secure voice and AI workflows across existing customer and enterprise systems. Value can be assessed through service capacity, response time, consistency and the proportion of work handled without manual intervention.
Use AI workflows to handle higher volumes of customer interactions consistently.
Coordinate voice and enterprise systems to support faster and more consistent service.
Reduce repetitive operational workload so frontline teams can focus on interactions requiring human judgement.
Move from reactive maintenance to earlier intervention.
Reactive maintenance and limited performance visibility can create avoidable downtime, disruption and underused assets. Atlas identifies performance patterns, anomalies and maintenance priorities across equipment and operations.
Identify performance patterns and opportunities to make more productive use of operational assets.
Surface anomalies and maintenance priorities earlier to reduce avoidable disruption.
Help teams direct maintenance resources towards the equipment and issues that require attention most.
See project risk earlier.
Project information is often dispersed across site, design, planning and commercial teams, making emerging risk harder to see and act on. Atlas connects relevant project and operational signals to surface dependencies, risk and performance issues earlier.
Identify emerging issues earlier so teams can intervene before they create avoidable rework.
Bring project and operational signals together to support faster decisions across teams.
Surface risks and dependencies earlier to help reduce delays and improve resource utilisation.
Extend monitoring across distributed infrastructure.
Manual inspection cannot scale across large distributed infrastructure networks. Runtime processes camera, vehicle, sensor or drone data locally and can classify issues and orchestrate the next action. Value can be assessed through inspection coverage, response time, field resource utilisation and earlier issue identification.
Process operational data locally to detect and classify issues closer to where they occur.
Use contextual information to determine which issues require attention first.
Direct field resources towards identified issues rather than relying solely on routine manual inspection.
Deploy enterprise AI within required controls.
Sensitive data, governance and audit requirements can constrain how organisations deploy AI. Runtime provides a controlled environment for models, agents and workflows within the required customer or sovereign infrastructure. Value can be assessed through deployment time, governance compliance, auditability and operational predictability.
Run intelligence within the required customer or sovereign infrastructure.
Apply controlled execution and governance to models, agents and workflows.
Provide greater visibility into how enterprise AI operates and changes over time.
Reduce manual processing across document-intensive work.
Manual intake, repeated data entry and disconnected evidence handling increase processing cost and cycle time. Runtime combines conversational intake, document and image understanding, governed AI agents and existing enterprise systems. Value can be assessed through processing time, manual effort, completeness of information and workflow throughput.
Capture and structure information earlier through conversational, document and image-based workflows.
Reduce repeated data entry and handling across document-intensive processes.
Bring evidence and enterprise information together so cases can be assessed more quickly and completely.
Turn site-level intelligence into a portfolio view.
Owners and operators can struggle to compare performance consistently across buildings, sites and asset classes. Atlas aggregates local intelligence into a portfolio view for benchmarking, prioritisation and cross-site learning. Value can be measured through portfolio operating cost, capital efficiency, asset performance and revenue opportunity.
Compare operational performance more consistently across sites, buildings and assets.
Identify where capital and improvement activity can be directed towards higher-value opportunities.
Identify recurring patterns and improvement opportunities across the wider portfolio.
See building performance in context.
Building data is often held across multiple systems, leaving facilities teams to respond after cost, energy or service issues are already visible. Atlas creates a connected view of building performance, maintenance, occupancy and operational workflow. Value can be measured through energy intensity, maintenance cost, response time, occupancy and service performance.
Connect operational information to identify inefficiencies and opportunities to improve building performance.
Give facilities teams better context to identify issues and respond more effectively.
Bring together maintenance, occupancy and performance information to support better use of building resources.
