Inteligencia de decisiones nativa de IA
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 motores
Simulación, optimización y análisis unificados en un solo API.
Más de 48 herramientas de IA
Acceso en lenguaje natural a predicciones, análisis e informes.
24/7/365
Cada activo tiene su propio científico de datos de IA siempre activo.
Demostración del bucle de decisión
Razonamiento de IA combinado con validación gemela.
Ajuste operativo
Diseñado para puertos, fábricas de semiconductores, calefacción urbana, centros de datos y fabricación, donde no se pueden ignorar las limitaciones físicas.
Resultados típicos
Capacidades de la plataforma
No es un tablero. No es un chatbot. Un flujo de trabajo completo, desde la ingesta de datos hasta el entrenamiento de modelos y la recomendación de decisiones, automatizado y funcionando las 24 horas.
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.
Cómo funciona
Paso 01
9 tipos de conectores, incluidos REST, MQTT, OPC UA, BACnet, Modbus, JDBC, CSV, Fabric y Templates. Las integraciones prediseñadas cubren Siemens, Honeywell, Kepware, PI, Azure y más.
Paso 02
Se seleccionan automáticamente 17 motores por tipo de pregunta. El razonamiento del gráfico de conocimiento rastrea la causalidad entre sistemas y cada resultado incluye una puntuación de confianza.
Paso 03
Twin Engine verifica los conflictos espaciales, la lógica del equipo y las restricciones de proceso en 3D antes de que las acciones aprobadas fluyan hacia los sistemas de ejecución.
Buceo profundo
FactVerse AI Agent es el motor de inteligencia de decisiones de la plataforma FactVerse. Combina LLMs de grado industrial, modelado matemático y optimización de investigación operativa.
Monitoreo en tiempo real de carriles automatizados. La IA detecta picos de pasajeros y simula configuraciones — reducción del 20-40% en tiempo de espera.
Cumplimiento ISO 14644-1 en tiempo real, predicción de vida útil de filtros HEPA, simulación OEE. Violaciones ISO reducidas en 90%.
Escenarios de la industria
La misma plataforma de motor de IA se puede implementar con plantillas de escenarios específicas de la industria manteniendo la coherencia de la infraestructura de simulación.
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.
Por qué FactVerse
Otros pueden mostrar, adivinar o representar. FactVerse puede mostrar, calcular, validar y ejecutar en un bucle cerrado.
| Capacidad | BI / Panel de control | Plataforma IoT | Consultoría en IA | 3D Gemelo digital | FactVerse |
|---|---|---|---|---|---|
| Ver problemas | |||||
| Comprender las causas | |||||
| Predecir tendencias | |||||
| simulación de IA | |||||
| Validación de física | |||||
| Visualización 3D | |||||
| Mejoramiento | |||||
| Pruebas de estrés | |||||
| Ejecución automática |
ROI de un vistazo
Basado en una instalación comercial típica con 100 activos administrados.
Ahorro de mantenimiento
$150K
/año por 100 activos
Reducción de energía
$200K
/año por edificio
Ahorro de tiempo del personal
720h
/año
período de recuperación
<6
meses
* Estimado en base a benchmarks de la industria para instalaciones de 50.000 m2. Los resultados reales varían según el tipo de instalación y la condición de los activos.
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.
Siguiente paso
FactVerse AI Agent está diseñado para equipos que necesitan más que paneles. Vea resultados mensurables en dos semanas con una prueba de concepto centrada en sus datos e instalaciones reales.
Descubre cómo este producto impulsa casos de uso reales.