Forgis FABRICA Platform

Forgis FABRICA turns fragmented factory data into optimized factories.

FABRICA

FABRICA is the industrial intelligence platform that turns fragmented machine, process, and product data into a continuously optimizing production system.

Designed for manufacturers and machine builders across mass production, high-mix low-volume, continuous process, and batch, where data silos, manual workflows, and vendor-locked interfaces keep plants at 70% OEE.

With FABRICA, manufacturers and machine builders monitor, optimize, and control production performance from a single unified interface, agnostic across equipment vendor, communication protocol, or hardware generation. The same data and intelligence surface differently for each stakeholder: production dashboards for plant managers, diagnostic and parameter tools for process engineers, real-time alerts and corrective actions for shop floor operators.

This enables manufacturers to minimize downtime, scraps, and energy consumption while maximizing quality, throughput, and robot/line repurposing speed, across brownfield and greenfield plants.

Platform Architecture

The FABRICA platform is built around 2 core components:

• Hyperion: Our Physical AI foundation model processes time-series (OPC UA, PLC scan-rate, MQTT Sparkplug B, vibration, energy-meter), documentation (manuals, schematics, SOPs, maintenance records, MES data), and spatial data (CAD models, digital twins, camera feeds) as native modalities, to build a continuously updated causal graph of production-line state from which fault attribution, degradation forecasting, and optimization signals derive.
• Fabrica Agents: purpose-built agents orchestrated by Fabrica across IT/OT, each informed by Hyperion's production states across machine, line, and plant levels to hit a specific use case.

Together, they transform raw production data into actionable insights to optimize uptime, quality, throughput, energy, and robot control in real time.

Reference Cases

Predictive maintenance: In body-in-white stamping presses, crankshaft bearings wear down silently under repeated load cycles, by the time degradation becomes visible, the component has already failed and the line has stopped. Forgis cross-compares synchronized bearing twins via vibration, acoustic, and temperature to flag degradation hrs before breakdown, pushing corrective actions to the HMI.

Quality defect detection: Vulcanization defects in rubber valve seals are caught by naked-eye inspection - inconsistent, late, and unable to connect what failed visually to what drifted in the process. We replace naked-eye inspection with camera-based detection and enables predictive quality by linking visual defects to mold temperature, pressure, and cooling time in real time.

Cycle time optimization: In multi-stage painting lines, the slowest stage - spray booth, flash-off zone, or oven - silently caps output for the entire line, with no visibility into where capacity is lost or why. We monitor stage-level performance continuously to locate the bottleneck and adjust operating parameters in real time to recover throughput without affecting quality.

Line repurposing: Adapting a welding cell to new parts, tools, or robot brands requires manual reprogramming of vendor-specific control code from scratch, weeks of external engineering per variant. We generate vendor-specific robot control, welding paths, and vision skills from natural language instructions, putting reconfiguration directly in the manufacturer's hands.