One platform.
Every project dimension.
Project Vision unifies schedule, cost, quality, risk, and HSE data across your entire capital project portfolio — turning scattered data sources into a continuously learning intelligence layer.
Every department. One unified view.
Eleven purpose-built surveillance pages cover the full capital project execution lifecycle. Each page reads from a live data layer and surfaces the signal that matters — including cross-domain signals that show when a problem in one area is about to affect another.
- Real-time cost, schedule, quality, materials, HSE, and commissioning tracking against your locked baseline
- Cross-domain signals surface when a materials shortage is about to block construction — before it happens
- Traffic-light thresholds set per project — every KPI card reflects your actual governance standards, not hardcoded defaults
- AI-generated plain English narrative with three specific recommended actions per page
Machine learning that explains itself.
Two layers of ML work in parallel. Gradient-boosted ensemble models forecast schedule delay, cost overrun probability, and quality trends from task and inspection records. A separate neural network forecasts how KPIs will move over time — trained on each project's own benchmark snapshot series.
- Ensemble models for task-level delay and quality predictions — trained per project, per discipline
- Neural network forecasts SPI, CPI, defect rate, and productivity trends from snapshot history with confidence intervals
- Feature importance surfaced in plain English — the three main drivers of each forecast, not a black box
- Human challenge mechanism: users correct wrong attributions, the model learns from accurate challenges over time
Four levels of intelligence, one platform.
A structured four-tier benchmarking hierarchy gives every stakeholder the right view at the right scope — from a single project's KPI percentile to anonymised industry-wide distributions that grow more accurate with every consented project that completes.
- View 1: single project ranked against comparable projects grouped by sector, type, and scale
- Views 2 & 3: Project Admin — customer portfolio or full cross-portfolio comparison with heatmap and cluster analysis
- View 4: industry intelligence — consent-gated, fully anonymised P10–P90 distributions, suppressed below three contributing projects
- Each completed consented project automatically enriches the FEA analogue pool and industry distributions
Estimate the next project from the last ten.
The Front-End Assessment engine embeds each completed project's profile and outcomes into a high-dimensional vector. Similarity search finds the closest analogues, then derives P10, P50, and P90 ranges from their real outcomes.
- Semantic embedding over project profile and outcome vectors — analogues found by meaning, not just category labels
- P10/P50/P90 cost and schedule distributions derived from the distribution of real analogue outcomes
- FID Assessment: four-dimension viability review with LLM narrative, admin review, full auditable decision trail
- Every project that completes and consents enriches future FEA estimates — accuracy compounds over time
Reliability engineering, built in.
FMEA → RCFA → SIL → RCM → Spares → Asset Health Score. The full reliability chain in one module, connected to your existing schedule and quality data.
Scope 1, 2, and 3 — no extra system.
Carbon footprint computed from data already in the platform. GRI and ISSB S2 disclosure PDF generated automatically. No separate ESG tool required.
The platform doesn't stop at handover.
Transition to Stage 6 and the dashboard switches to operations mode. FMEA flows into live bad-actor rankings. Predictive maintenance activates.
Unified data ingestion
Import schedule (P6 XER, MS Project MSPDI), cost, manpower, materials, HSE, commissioning, compliance, energy, ESG social, and environmental data from any format. AI-assisted column mapping resolves terminology differences automatically. 14 data domains, all in one platform.
Ensemble models, neural networks, and portfolio ML
Ensemble models forecast task-level outcomes per discipline. A neural network forecasts KPI trends with confidence intervals. Portfolio-level cross-project models predict equipment reliability and contractor quality patterns across all consented projects.
Cross-domain signal engine
A declarative influence registry tracks causal relationships between data domains — materials shortages that will block construction, productivity declines driving cost overruns, HSE leading indicators predicting incident risk. Signals surface automatically on every surveillance page and in the APM 30/60/90-day calendar.
Built-in ESG and carbon reporting
Scope 1, 2, and 3 emissions computed at import from canonical emission factor libraries (NGER 2024, EU EN 15940, ICE v3.3). GRI 302/305/401/403/413 and ISSB S2-aligned disclosure PDF generated from the same canonical tables. TCFD risk tagging, ESG target on-track indicators, and third-party assurance-ready data trails included.
Role-aware, multi-project
Built for organisations managing multiple projects across multiple customers. Role-based access, per-user use case gating, and customer data isolation are foundational — not bolted on. Admins see everything; viewers see exactly what they need. APM and ESG modules gate separately to the relevant team members.
Full lifecycle — construction to operations
Six project stages from FEA through to Operations and Historical. Transitioning to Stage 6 unlocks the operational dashboard (availability, throughput, unit cost), two new import tabs, and the predictive maintenance engine. Construction FMEA flows into operational bad-actor rankings. The platform grows with your asset.