Spatial Intelligence Layer
Geo-distributed Fleet intelligence collaboration
Vehicles and devices that learn routes, predict demand, and optimize logistics over time.
The spatial intelligence layer for the [&] stack. Digital twin synchronization, federated fleet learning, and geo-distributed state that converges — not conflicts.
GeoFleetic gives AI agents spatial awareness — not just GPS, but genuine understanding of routes, territories, and movement patterns that evolve.
Spatial digital twin technology for real-time fleet state. Each vehicle maintains a local digital twin synchronized via delta-CRDTs — convergence under network partitions without central coordination. Tile38-inspired geofencing with continual learning layered on top.
Spatial Digital Twins · Tile38Federated fleet learning — each vehicle trains local models on route data, then shares only model deltas (not raw data). Decentralised model exchange addresses distribution shifts while preserving privacy. Fleet knowledge compounds without centralizing data.
Federated Learning · LoRAGraph neural network route optimization that improves over time. Spatial-temporal patterns learned continually — no retraining. Paired with TickTickClock for temporal context, creating a complete when-and-where intelligence layer.
GNN · Continual SpatialGeoFleetic implements methods from distributed systems research, spatial computing, and federated machine learning.
Inspired by GeoCoCo (2025) — conflict-free replicated data types with epoch isolation. Merge functions satisfy ACI properties. Network partitions affect only latency, never correctness. Fleet state converges deterministically after reconciliation.
Decentralised model exchange inspired by continual learning principles for federated settings. Lightweight LoRA adapters shared across fleet members enable rapid adaptation to new routes without transmitting raw GPS data or retraining base models.
IEEE COMST 2025 research demonstrates digital twins enabling real-time coordination among heterogeneous agents — vehicles, roadside units, and traffic control centers. GeoFleetic implements this with edge-to-cloud data pipelines and MCP-based tool connectivity.
Every GeoFleetic endpoint is an MCP server. Anthropic's MCP (agent-to-tool) and Google's A2A (agent-to-agent) protocols let fleet agents query spatial state, subscribe to geofence events, and coordinate route changes through standardized interfaces.
GeoFleetic is the Intelligence layer — spatial awareness where TickTickClock provides temporal. Together: where and when.
Each driver's local model captures neighborhood patterns — parking, building access, customer timing. Federated sharing means fleet-wide quality improves without centralizing driver data.
Pair with TickTickClock for time-aware dispatch. Technician routing that accounts for skill proximity, traffic patterns, and historical job duration at each location — continually improving.
Road conditions, construction zones, and hazards synchronized via CRDTs across all vehicles. Each observation improves the shared spatial model. Edge-native decisions with fleet-wide context.
Warehouse-to-store spatial models that learn regional demand. Seasonal shifts, local events, and weather correlations in Graphonomous-powered spatial knowledge graphs that evolve without retraining.
Part of the [&] Ampersand Box Design portfolio — the infrastructure that makes AI learn, adapt, and decide. Elixir/OTP. Edge-native. MCP-first.
GitHub Explore [&]