01Technology
A layered architecture for deterministic, observable logistics intelligence.
The platform is composed of independently scalable services connected through a typed event bus. Each layer exposes versioned, documented contracts.
02System architecture
Stage 01
Ingest
Stage 02
Feature store
Stage 03
ML models
Stage 04
Orchestration engine
Stage 05
Integration layer
observability · audit · access control · spans every stage
Layer 01L1
Predictive layer
Ensemble ML models trained on multi-year operational telemetry produce demand forecasts, ETA distributions, and bottleneck probabilities.
- Gradient-boosted decision trees + sequence models
- Continuous re-training with drift detection
- Confidence-interval outputs, not point estimates
Layer 02L2
Optimization layer
Mixed-integer and constraint solvers translate forecasts into executable plans across routing, dispatch and resource allocation.
- Sub-second incremental re-optimization
- Multi-objective: cost, latency, emissions, SLA
- Explainable decisions with diff against prior plan
Layer 03L3
Integration middleware
A typed integration layer translates between legacy ERP/WMS contracts and platform events, with backward-compatible schema evolution.
- First-party connectors for SAP and Oracle
- Idempotent writes with full event sourcing
- Per-field PII masking and access policies
Layer 04L4
Observability & control
End-to-end telemetry, audit trails, and operator override controls. Every decision is reproducible from inputs and model version.
- OpenTelemetry traces across all services
- Immutable decision log with cryptographic chain
- Role-scoped manual override with reason codes
04Reference stack
Built on proven, auditable infrastructure.
KubernetesPostgreSQLApache KafkagRPCOpenTelemetryRedisS3-compatible object storageVaultTerraformPython · Go · TypeScript