AI Schedule Risk Intelligence

Core Logic

Uses GPT-4 Turbo as central workflow controller. Initiates 6-phase workflow (assembly, ingestion, analysis, scenario generation, simulation, synthesis), manages agent dependencies, handles inter-agent communication, and coordinates result aggregation. Tracks workflow phase progression and ensures completion before executive summary generation.

Core Logic

Uses Claude 3 Haiku for lightweight, fast data collection. Connects to Primavera P6 and Procore APIs, extracts schedule activities, resource assignments, and progress data. Validates data integrity, identifies gaps, and normalizes formats for downstream analysis. Serves as single source of truth for all agents.

Core Logic

Combines GPT-4 with XGBoost ML model for hybrid AI/ML risk prediction. Analyzes multiple risk factors simultaneously, assigns probability scores to activities, identifies cascading risk dependencies, and prioritizes risks by potential schedule impact. Provides explainable predictions with feature importance rankings.

Core Logic

Uses Claude 3 Sonnet with CPM algorithm implementation. Calculates forward/backward pass, identifies critical path and near-critical activities, computes total/free float, and analyzes logic relationships. Highlights activities where delays would directly impact project completion and quantifies float consumption rates.

Core Logic

Combines GPT-4 reasoning with Google OR-Tools optimization solver. Analyzes resource loading across activities, identifies conflicts and over-allocations, generates optimized resource schedules, and recommends reallocation strategies. Balances schedule compression against resource constraints.

Core Logic

Uses GPT-4 Turbo to generate multiple recovery scenarios based on current delay status. Creates options including acceleration (overtime, additional crews), re-sequencing, scope adjustment, and resource reallocation. Calculates cost, schedule, and risk implications for each scenario with success probability estimates.

Core Logic

Uses NumPy/SciPy for high-performance Monte Carlo simulation. Runs 10,000+ iterations with three-point duration estimates, generates probability distributions for milestone completion, calculates P10/P50/P90 completion dates, and identifies activities with highest schedule sensitivity. Provides confidence intervals for all forecasts.

Core Logic

Uses Claude 3 Opus for sophisticated natural language synthesis. Aggregates findings from all analysis agents, prioritizes recommendations by impact and urgency, generates executive-ready summaries with supporting evidence, and creates stakeholder-specific views. Produces final executive dashboard content.

Core Logic

Uses GPT-4 with legal/regulatory database access. Monitors compliance status across six categories (safety, environmental, labor, permits, quality, contractual), identifies violations and risks, tracks automated compliance checks, and flags issues requiring attention. Ensures schedule recovery scenarios maintain compliance.

Core Logic

Uses Claude 3 with GHG Protocol methodology. Calculates Scope 1/2/3 carbon emissions, tracks sustainability metrics (recycled materials %, waste reduction, energy efficiency), monitors social impact (diversity, safety incidents, training), and reports governance compliance. Projects emissions impact of schedule recovery scenarios.

Core Logic

Combines Prophet and ARIMA models for time-series forecasting. Tracks five market categories (materials, labor, equipment, fuel, utilities), provides price trends with volatility indicators, generates 30/60/90-day forecasts, and identifies cost risks for acceleration strategies. Informs scenario cost estimates with current market conditions.

Core Logic

Combines XGBoost and LSTM models for safety incident prediction. Analyzes leading indicators (near-misses, observations, training compliance), calculates incident probability for activities, identifies risk factors, and recommends preventive controls. Ensures schedule recovery scenarios include safety risk mitigation.