KI-native Entscheidungsintelligenz
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 Motoren
Simulation, Optimierung und Analyse vereint unter einem API.
48+ KI-Tools
Zugriff in natürlicher Sprache auf Vorhersagen, Analysen und Berichte.
24/7/365
Jede Anlage erhält einen eigenen, ständig verfügbaren KI-Datenwissenschaftler.
Entscheidungsschleifen-Demo
KI-Argumentation gepaart mit doppelter Validierung.
Operative Passform
Entwickelt für Häfen, Halbleiterfabriken, Fernwärme, Rechenzentren und die Fertigung, wo physische Einschränkungen nicht ignoriert werden können.
Typische Ergebnisse
Plattformfunktionen
Kein Dashboard. Kein Chatbot. Ein vollständiger Workflow von der Datenaufnahme über das Modelltraining bis hin zur Entscheidungsempfehlung, automatisiert und rund um die Uhr laufend.
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.
Wie es funktioniert
Schritt 01
9 Connector-Typen, darunter REST, MQTT, OPC UA, BACnet, Modbus, JDBC, CSV, Fabric und Templates. Vorgefertigte Integrationen umfassen Siemens, Honeywell, Kepware, PI, Azure und mehr.
Schritt 02
17 Engines werden automatisch nach Fragetyp ausgewählt. Das Wissensdiagramm-Argumentation verfolgt systemübergreifende Kausalitäten und jedes Ergebnis umfasst eine Konfidenzbewertung.
Schritt 03
Twin Engine prüft räumliche Konflikte, Gerätelogik und Prozessbeschränkungen in 3D, bevor genehmigte Aktionen in Ausführungssysteme einfließen.
Tiefer Einblick
FactVerse AI Agent ist die Entscheidungsintelligenz-Engine der FactVerse-Plattform. Sie kombiniert industrietaugliche LLMs, mathematische Modellierung, Simulationsengines und Operations-Research-Optimierung.
Echtzeit-Überwachung automatisierter Spuren. KI erkennt Passagierspitzen und simuliert Spurkonfigurationen — 20-40% Reduzierung der Wartezeit.
Echtzeit ISO 14644-1 Compliance, HEPA-Filter-Lebensdauervorhersage, OEE-Simulation. ISO-Verstöße um 90% reduziert.
Branchenszenarien
Dieselbe KI-Engine-Plattform kann mit branchenspezifischen Szenariovorlagen bereitgestellt werden, während die Simulationsinfrastruktur konsistent bleibt.
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.
Warum FactVerse
Andere können zeigen, raten oder rendern. FactVerse kann in einer geschlossenen Schleife anzeigen, berechnen, validieren und ausführen.
| Fähigkeit | BI / Dashboard | IoT-Plattform | KI-Beratung | 3D Digitaler Zwilling | FactVerse |
|---|---|---|---|---|---|
| Probleme sehen | |||||
| Ursachen verstehen | |||||
| Trends vorhersagen | |||||
| KI-Simulation | |||||
| Physikalische Validierung | |||||
| 3D-Visualisierung | |||||
| Optimierung | |||||
| Stresstest | |||||
| Automatische Ausführung |
ROI auf einen Blick
Basierend auf einer typischen Gewerbeanlage mit 100 verwalteten Vermögenswerten.
Wartungseinsparungen
$150K
/Jahr pro 100 Assets
Energiereduzierung
$200K
/Jahr pro Gebäude
Zeitersparnis für das Personal
720h
/Jahr
Amortisationszeit
<6
Monate
* Geschätzt basierend auf Branchen-Benchmarks für 50.000 m² große Einrichtungen. Die tatsächlichen Ergebnisse variieren je nach Anlagentyp und Anlagenzustand.
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.
Nächster Schritt
FactVerse AI Agent wurde für Teams entwickelt, die mehr als nur Dashboards benötigen. Sehen Sie messbare Ergebnisse in zwei Wochen mit einem gezielten Proof of Concept auf Ihren realen Daten und Ihrer realen Anlage.
Sehen Sie, wie dieses Produkt reale Anwendungsfälle unterstützt.