Domain Applications
Same Agentic Engine. Three Proven Domains.
GoatAI is not a flood company that also does mobility. These are three proof points that the physics-grounded agentic reasoning abstraction works — domain-agnostically. The agents stay the same. Only the physics data changes.
Proof Across Domains
Same reasoning. Different physics data. Different outputs.
Each domain runs the same four-agent orchestration cycle — Monitoring, Prediction, Reasoning, Decision — with domain-specific physics engines and data sources.
Crane & Yard
Clearance & Position Reasoning
Recognition-based clearance management and load monitoring over overhead cranes and the slab & coil yard.
- HookVision — crane anti-collision, fusing camera + radar (in development)
- Novelty and deviation flagging against the bay's normal state
- YardVision — fixed-optical slab & coil position and load monitoring (in design)
Same reasoning architecture as the furnace and ladle. Here the physics is crane kinematics and clearance geometry — and a deterministic barrier always owns the stop.
Explore Crane & Yard →
Furnace & Converter
Pulpit Visibility & Endpoint Reasoning
Operating-pulpit video and physics surrogates over the EAF and BOF converter for visibility, endpoint, and quality reasoning.
- HeatVue — EAF operating-pulpit video wall, 4×5MP GMSL2, ≤60 ms lens-to-output (Rev F offer)
- Furnace shell and lance-interaction zone coverage
- BOF surrogates — reduced-order models + ML for endpoint & quality reasoning (research)
Same reasoning architecture as the crane and ladle. Here the physics is BOF two-zone kinetics and metallurgical endpoint models.
Explore Furnace & Converter →
Ladle & Caster
Ladle Condition & Solidification Reasoning
Ladle visualization across the DE bay and solidification surrogates over the continuous caster.
- LVS — ladle ID, lining condition, lift/landing safety, slag & skull (in design)
- DE bay coverage: 36 ladle stands, 3 cranes, 15 m hook height
- Caster surrogates — solidification ROM for quality reasoning (research)
Same reasoning architecture as the crane and furnace. Here the physics is heat transfer and caster solidification.
Explore Ladle & Caster →
The Abstraction
One system. Three proofs.
The Agentic Abstraction
Monitoring Agent
Detects anomalies in real-time across any physical system
Prediction Agent
Applies physics models to forecast future system state
Reasoning Agent
Synthesizes data and physics into explainable risk assessments
Decision Agent
Generates recommendations and triggers autonomous responses
These four agents run in every domain. Physics engines swap. Agents stay constant.
Environmental
Data: Satellite + weather + IoT sensors + GIS terrain
Physics: HRF-SWE hydrology, kriging, shallow water equations
→ Flood alerts · Watershed forecasts · Water quality reports
Infrastructure
Data: SCADA + asset sensors + structural logs + geospatial hazard
Physics: Structural FEM, failure mode analysis, asset stress modeling
→ Asset health alerts · Maintenance triggers · Risk reports
Mobility
Data: Vehicle telemetry + transit feeds + incident reports + infrastructure state
Physics: Traffic flow models, congestion propagation, route optimization
→ Route recommendations · Congestion alerts · Timing optimization
Expanding
Additional domains in development
The agentic architecture is designed to extend to any physical domain where physics-grounded reasoning improves operational outcomes.
Industrial Operations
Plant intelligence, process monitoring, operational resilience
Agriculture Intelligence
Crop stress modeling, land-use optimization, water-agriculture coupling
Environmental & Forest
Ecosystem monitoring, terrain analysis, forest analytics
Multiphysics Simulation
PDE-driven scenario simulation, predictive modeling
Understand the platform behind all three domains
The agentic orchestration layer — architecture, agents, physics integration