From Fragmented Execution Data to Predictive Decisions
The execution of renewable energy projects is complicated, as the successful execution depends on linking project data, processes, and risks to a single, predictive system.
Our Project Intelligence Platform transforms fragmented execution data into an AI-powered decision-making tool, helping teams forecast risks, mitigate delays, and enhance results.
The Project Intelligence Platform is a predictive execution intelligence system specifically designed for renewable energy projects. It merges existing project systems into one execution model that:
01Predicts risks to schedules and purchasing.
02Performs automatic analysis of the reasons for issues.
03Models the results of possible decisions prior to taking action.
04Suggests ways to recover.
05Presents information using displays, notifications, and conversational questioning.
Unlike ordinary reporting software, the platform recognizes the order of execution and the connections throughout the project’s network.
The Intelligence Loop
Main Features & Capabilities
Connected capabilities work on one execution model — each building on the last, from early risk signals to root causes, recovery suggestions, tested scenarios, schedule decisions and unified KPIs. Select a stage to explore it.
Enterprise Project Environment
The Compatibility
The platform is compatible with systems frequently used in renewable energy execution:
Project scheduling software Primavera P6, MS Project, and so on
ERP purchasing and stock systems SAP, Oracle, and so on
Daily progress logs and productivity statistics
Systems used by contractors to carry out work
Processes for quality inspection and approvals
Weather information services
Repositories of engineering changes
Project Intelligence PlatformOne execution model
Recognizes the order of execution and the connections throughout the project’s network
STAGE 01Predict / 06
Predictive Risk Notifications
Creates early notifications of potential schedule setbacks, supplier delays, or shortages of personnel.
Quickly determines the reasons for delays, with quantifiable effects like problems with vendor deliveries, pending approval of quality, interruptions from the weather, and declining productivity.
RiskInvestigationRoot causeUnderstanding
STAGE 03Suggest / 06
AI-Based Suggestions
Gives practical advice such as reorder tasks, reassign workforces, equipment, speed up particular purchases, and enable the simultaneous safe completion of tasks.
AUnderstand
Causes & risks
Root causes and predicted risks from the execution model.
BSuggest · practical advice
Reorder tasks
Reassign workforces and equipment
Speed up particular purchases
Enable the simultaneous safe completion of tasks
CAct
Ways to recover
Recovery options for teams to decide on and act.
STAGE 04Model / 06
“What-If” Modelling
Allows groups to create models of alternative project execution approaches and to compare outcomes in terms of:
Schedule effect
Cost difference
Probability of risk
Utilization of resources
CurrentProject execution approach
A
ScenarioAlternative approach A
B
ScenarioAlternative approach B
Compare outcomesAB
Schedule effect
Cost difference
Probability of risk
Utilization of resources
Evaluate before taking action
STAGE 05Schedule / 06
Smart Scheduling Across the Platform
Smart scheduling is the natural outcome of project intelligence — from the scheduling systems the platform connects to, through risk predictions, recommendations and tested scenarios, to the schedule decisions teams use to steer execution.
01Schedule data
Connects to project scheduling software such as Primavera P6 and MS Project.
02Predictions
Predicts risks to schedules and creates early notifications of potential schedule setbacks.
03Recommendations
Suggests reordering tasks, reassigning workforces and equipment, and enabling the simultaneous safe completion of tasks.
04Schedule & execution
Compares the schedule effect of alternative approaches and tracks schedule deviation and percentage of work completed.
STAGE 06Monitor / 06
Unified KPIs + Predictive Intelligence
Supplies both current and predictive measurements, including schedule deviation, percentage of work completed, stock availability, equipment use, and anticipated obstructions and risks of delay.
Same execution model
01Risk notifications
02Root causes
03AI-based suggestions
04What-if scenarios
05Smart scheduling
Unified KPIsCurrentPredictive
Schedule deviation
Percentage of work completed
Stock availability
Equipment use
Anticipated obstructions and risks of delayPredictive
OutcomeProject visibility
OutcomeDecision support
AI Reasoning
How does it work?
Five steps take live project data from connected systems to the insights each team uses — with AI reasoning at the centre.
01
Data Integration
Safe connections take live data from various systems.
02
Data Standardization
Cleaning and conversion to a single model.
03
Building Project Execution Context
AI learns procedures, connections, and business rules.
04
AI Reasoning
AI with multiple agents provides KPIs, alerts, causes, simulations, and recommendations.
KPIsAlertsCausesSimulationsRecommendations
05
Insight Provision
Via displays, notifications, interactive questioning, and reports.