AI-Native Decision Intelligence
AI computes the optimal. Physics engine validates the feasible. You see it before you commit.
The AI Data Scientist for every operational asset. FactVerse AI Agent combines multiple simulation and optimization engines, a knowledge graph, and the FactVerse 3D Twin Engine to turn operational questions into validated, executable decisions — across border operations, semiconductor fabs, district heating, data centers, and manufacturing.
17 Engines
Simulation, optimization & analysis unified under one API
48+ AI Tools
Natural language access to prediction, analysis & reporting
24/7/365
Every asset gets its own always-on AI data scientist
Decision Loop Demo
AI reasoning paired with twin validation
Operational Fit
Built for ports, semiconductor fabs, district heating, data centers, and manufacturing — where physical constraints cannot be ignored.
Typical Outcomes
Platform Capabilities
Not a dashboard. Not a chatbot. A complete data science workflow — from data ingestion to model training to decision recommendation — automated and running 24/7 for each asset individually.
Unified What-If API orchestrates multiple simulation, optimization, and analysis engines — DES, Monte Carlo, NSGA-II, MILP, Agent-Based Modeling, System Dynamics, Bayesian Optimization, Causal Inference, Survival Analysis, and more. One API call, automatic engine selection, baseline vs modified comparison with confidence intervals.
Hundreds of nodes, hundreds of relationships, and dozens of entity types connect equipment, sensors, alerts, work orders, and causal chains across your facility. When an anomaly occurs, the knowledge graph traces root causes across subsystems — connecting a heating failure to downstream checkpoint performance in seconds.
Holt-Winters forecasting with confidence bands, Kalman filtering for sensor fusion, Weibull reliability analysis for equipment lifespan, Conformal Prediction for calibrated uncertainty, and automated anomaly detection via Isolation Forest. Every prediction includes a confidence score, not just a point estimate.
48 registered AI tools accessible through natural language. Operators ask questions, launch simulations, compare scenarios, and generate reports — all through conversation. Supports both cloud-hosted and on-premise LLM providers with streaming responses and automatic context injection.
Push analytics from centralized dashboards to individual assets. Each checkpoint lane, chiller, production line, or heat exchanger gets its own AI analyst — continuously monitoring, predicting, and optimizing. Scale from 3 assets to 300 without adding data science headcount.
From sensor anomaly to AI analysis to confidence-scored recommendation to human approval to work order creation to execution tracking to effect verification. Every decision is logged with full audit trail for compliance. Integrates directly with BMS, SCADA, and CMMS.
How It Works
Step 01
9 connector types — REST, MQTT, OPC UA, BACnet, Modbus, JDBC, CSV, Fabric, Templates. Pre-built integrations for Siemens, Honeywell, Kepware, PI, Azure. AI auto-maps sensors to models.
Step 02
17 engines auto-selected by question type. Knowledge graph traces cross-system causality. 48+ AI tools accessible via natural language. Every result includes confidence scoring.
Step 03
Twin Engine checks spatial conflicts, equipment logic, and process constraints in 3D. Approved actions flow to BMS/SCADA/CMMS. Full audit trail for compliance.
Deep Dive
Every operations-intensive enterprise knows there's gold in their data. The bottleneck isn't data — today's sensors, SCADA, and BMS generate terabytes per second. The bottleneck is: who analyzes it?
A human data scientist who understands statistical modeling, machine learning, and physical facility operations costs $150–250K/year and can serve 3–5 projects at a time. You have 43 checkpoint lanes, 200 chillers, 15 SMT production lines, 80 heat exchangers — each with different operating patterns, each needing independent analysis.
FactVerse AI Agent gives every asset its own AI analyst. Not a dashboard. Not a chatbot. A complete data science workflow — from data ingestion to model training to decision recommendation to execution — automated end-to-end and running 24/7/365.
Deep Dive
All simulation, optimization, and analysis engines share a single What-If API. Ask any "what happens if..." question — the platform automatically selects the right engine, runs baseline vs. modified scenarios, and returns results with confidence intervals.
Simulation: Discrete Event Simulation, Agent-Based Modeling, Monte Carlo, System Dynamics
Optimization: NSGA-II multi-objective, MILP exact solving, Genetic Algorithms, Bayesian search, CP-SAT constraint solving
Prediction: Holt-Winters forecasting, Kalman filtering, Conformal Prediction intervals
Analysis: Causal inference (DoWhy), Survival analysis, Weibull reliability, DOE/Sobol sensitivity
The key advantage: for the same problem, FactVerse can simultaneously run Erlang-C (analytical solution) + DES (simulation validation) + Monte Carlo (stress testing) + Network Flow (bottleneck analysis) — four perspectives cross-validating each other. This is physically impossible with human analysts.
Deep Dive
Most AI platforms stop at "best score." FactVerse adds a second engine — the Twin Engine validates AI recommendations in a physics-aware 3D digital twin before anyone acts on them.
AI says "open 15 lanes to reduce wait time below 8 minutes" — but lanes 7 and 12 are only 3 meters apart, and opening both creates a crowd crush risk. The Twin Engine catches this. AI says "start chillers 1, 3, and 5 simultaneously" — but chiller 3's exhaust duct is above the fire escape route. The Twin Engine flags it.
Three-layer validation: AI Engine computes the optimal → Twin Engine validates the feasible → Real-world execution with live feedback correction.
Deep Dive
Hundreds of nodes and relationships link equipment, sensors, alerts, work orders, chillers, pumps, and motors across your facility. When something goes wrong, the knowledge graph traces causality across subsystems:
Transit hub wait times rising → knowledge graph traces → HVAC system fault → checkpoint overheating → passenger complaints increasing.
This cross-module causal reasoning is what separates FactVerse from point solutions that can only see their own domain.
Deep Dive
Every decision follows a complete cycle: sensor anomaly → AI analysis → confidence-scored recommendation → human approval → work order creation → execution tracking → effect verification. Each step is logged with full audit trail for regulatory compliance.
The platform integrates directly with BMS, SCADA, and CMMS systems to close the loop from digital insight to physical action — zero delay from recommendation to execution.
Industry Scenarios
Same AI engine platform, industry-specific modules. Each new industry deploys with scenario templates — the simulation infrastructure is universal.
Dozens of automated lanes monitored in real-time. AI detects passenger surges, predicts flow with Holt-Winters, simulates lane configurations via DES, validates with Erlang-C queuing theory, and stress-tests with tens of thousands of Monte Carlo runs — all in under 60 seconds. Result: 20-40% reduction in average wait time.
Real-time ISO 14644-1 compliance monitoring, particle counting at 0.1/0.5/5μm, HEPA filter lifespan prediction via Weibull analysis, SMT production line OEE simulation, and chiller COP optimization. Reduces ISO violations by 90% and increases planned filter replacements to 95%.
MPC model predictive control for supply/return temperature optimization, weather API integration for 24-hour load forecasting, hydraulic balance analysis for valve optimization, and automated pre-heating strategies before cold snaps. End-user temperature compliance: 85% → 98%.
Thermal distribution prediction, Bayesian optimization for cooling parameters, System Dynamics capacity planning, and automated Green Mark/LEED/ISO 50001 reporting. Target: reduce the 40% of energy spent on cooling by 15-30%.
Discrete event simulation for production line optimization, AI-driven defect classification, causal inference (DoWhy) for environment-yield correlation, and XR-based operator training. Supports WEF Lighthouse factory criteria.
Why FactVerse
Others can show, guess, or render. FactVerse can show + compute + validate + execute — full closed loop.
| Capability | BI / Dashboard | IoT Platform | AI Consulting | 3D Digital Twin | FactVerse |
|---|---|---|---|---|---|
| See problems | |||||
| Understand causes | |||||
| Predict trends | |||||
| AI simulation | |||||
| Physics validation | |||||
| 3D visualization | |||||
| Optimization | |||||
| Stress testing | |||||
| Auto-execution |
ROI at a Glance
Based on a typical commercial facility with 100 managed assets.
Maintenance savings
$150K
/year per 100 assets
Energy reduction
$200K
/year per building
Staff time saved
720h
/year
Payback period
<6
months
* Estimated based on industry benchmarks for 50,000 sqm facilities. Actual results vary by facility type and asset condition.
FAQ
It's an AI-driven simulation and decision platform for complex physical facilities. Think of it as an always-on AI data scientist for each of your operational assets — combining multiple simulation engines, a knowledge graph, and a 3D digital twin validation layer. It doesn't just analyze data; it simulates alternatives, validates feasibility, and recommends executable actions.
BI dashboards show you what happened. IoT platforms show you what's happening now. FactVerse AI Agent shows you what will happen next and what you should do about it — then validates that recommendation against your facility's physical constraints before you act. It's the difference between monitoring and decision intelligence.
17 engines unified under one API: Discrete Event Simulation (SimPy), Agent-Based Modeling, Monte Carlo, System Dynamics, NSGA-II/III multi-objective optimization, MILP, Genetic Algorithms, Bayesian Optimization, CP-SAT constraint solving (Google OR-Tools), Holt-Winters forecasting, Kalman Filter, Conformal Prediction, Causal Inference (DoWhy), Survival Analysis, Weibull Reliability, DOE/Sobol sensitivity, and Distribution Fitting.
AI engines compute the mathematically optimal solution. Then the FactVerse Twin Engine validates it in a physics-aware 3D environment — checking for spatial conflicts, equipment logic violations, and process constraints. Most competitors have only the AI layer. FactVerse adds the physical validation layer, raising confidence from "mathematically correct" to "operationally feasible."
Most customers see measurable impact within a 2-week proof of concept, starting with a single facility area. Typical ROI: $150K+ maintenance savings per 100 assets, $200K+ energy reduction per building, 720+ staff hours saved per year. Payback period is typically under 6 months.
DFS Lite supports 9 connector types: REST API, CSV, JDBC, MQTT, Microsoft Fabric, OPC UA, BACnet (bridged), Modbus (bridged), and pre-built templates for Siemens, Honeywell, Kepware, OSIsoft PI, and Azure. AI auto-mapping handles sensor-to-model linking automatically.
Next Step
FactVerse AI Agent is for teams that need more than dashboards. See measurable results in 2 weeks with a focused proof of concept on your real data and real facility.
See how this product powers real-world use cases.