Shared operational data
Shipments, assets, journeys and events form the proposed network model. Source timing and dependencies give teams a common context for planning.
Models & engines / Logistics
LogiWit
A decision model for connected logistics.
Inside LogiWit: an operational data model, disruption scenarios and a planned intelligence layer for human-led logistics decisions.
Discuss your project
Development status / Concept preview
Illustrative software concept. The public preview uses predefined scenarios; no live telemetry, AI model or automatic operational execution is connected.
LogiWit
Models, agents & system design
Forecasting and scenario design
The target intelligence layer identifies bottlenecks and compares route or capacity changes. The website demonstrates scenario logic with illustrative values, not a deployed predictive model.
Human-controlled decisions
Recommendations are intended to show assumptions and downstream effects before approval. A future agent workflow must preserve this review boundary; the preview executes no operational changes.
How the system fits together
- 01Network data
- 02Forecasts & scenarios
- 03Review & approve
LogiWit
Models & engines
Connected logistics data model
Inside LogiWit's concept for linking shipments, assets, facilities, journeys and events in one operational context with source timing preserved.
Logistics scenario engine
How LogiWit's concept preview compares route disruption and capacity scenarios, showing assumptions and trade-offs before a human decision.
LogiWit
Capabilities
Earlier project material +
Fleet & Twin Setup
Onboard fleets, modes (road/sea/air/rail), regions, and data sources; configure SLAs, policies, and optimization goals.
Multimodal Routing (RL)
RL-driven VRP with time windows & constraints (drivers, docks, tides, flight slots) for lowest cost-to-serve plans.
ETA Prediction
ML ETAs using traffic, weather, port dwell, and handoff risk; per-leg and end-to-end confidence bands.
Port & Yard Vision (YOLO)
Detect ships, trucks, containers, cranes; generate congestion indices and dwell heatmaps from aerial/satellite feeds.
Container & Yard Occupancy
Real-time slot utilization, stack counts, and berth readiness for smarter gate/yard scheduling.
RUL & Predictive Maintenance
Sequence models on RPM, temps, vibrations, and fault codes to predict remaining useful life and avoid breakdowns.
More capabilities + 12
Sensor & Telemetry Anomalies
Autoencoders & Isolation Forest to flag drift, spoofing, or failing sensors; route incidents to maintenance.
Driver Behavior Scoring
Identify harsh events, idling, speeding; coach to reduce fuel burn and incidents—feed back into routing costs.
CO₂ & Cost-to-Serve
Lane-level emissions & all-in cost models; optimize plans under carbon or budget constraints.
Packing & Stowage Optimizer
Weight/CoG/sequence-aware loading plans for containers & trailers using ILP + learned heuristics.
Twin Simulation & What-If
Fast surrogate simulators + Bayesian filters to test disruptions (weather, strikes) before they hit operations.
Ops Copilot (LLM + RAG)
Ask the twin in natural language: “why late?”, “best plan if Port A closes?”, “CO₂ if we divert via B?”—with citations.
RESTful API & SDK
Use Python/JS SDKs or call REST to embed routing, ETA, and alerts into TMS/WMS, OMS, or your internal dashboards.
Economic & Disruption Engine
Simulate weather, strikes, capacity shocks, and port delays; auto-generate resilient playbooks.
Batch Network Analysis
Score thousands of lanes and assets per job with SLA-backed outputs for planning cycles.
Cloud & On-Prem Ready
Deploy securely in your VPC or use our managed cloud with Docker/Kubernetes support.
Live Operational Views
Interactive maps, ETAs, congestion tiles, and incident feeds for dispatch, control towers, and port ops.
24/7 Support
Assistance with deployment, model tuning, integrations, and custom features—around the clock.
Inside the project
Project gallery
GATAI
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