01
−18%
miles per delivery
Real-time route optimization
Routing for fleet-scale operations with exception handling, driver-behavior calibration, and TMS / WMS integration. Dispatcher UIs co-built with operations during rollout.
AI & Machine Learning × Logistics & Supply Chain
AI for Logistics & Supply Chain.
Real-time route optimization, ETA prediction, demand forecasting, and reinforcement-learning dispatch, co-built with operations teams during rollout, calibrated to driver-behavior reality, integrated with the TMS / WMS dispatch ops actually use.
- DOT / FMCSA
- Aware
- ELD compliance
- Aligned
- C-TPAT
- Supported
- SOC 2 Type II
- In flight
The reality
Logistics AI fails when the model competes with the dispatcher instead of working with them.
The pattern: a routing model that outperforms the legacy heuristic in backtest but breaks under exception load; a dispatcher UI that ignores the constraints dispatchers actually weigh; an ETA prediction grounded in lab telemetry rather than driver-behavior reality; a demand forecast that ignores promotion calendars; and an optimization engine that learns nothing from the manual overrides dispatchers make all day. Logistics AI succeeds when the model, the UI, and the operations team are co-designed.
Prosigns ships logistics AI co-built with the people who run dispatch. Senior engineers sit in dispatch centers during design, ride with drivers, and learn the override patterns the legacy system ignored. RL dispatch engines learn from dispatcher overrides explicitly. ETA models calibrated to driver behavior, not lab conditions. Customer-facing tracking with the freshness operations actually requires.
Where it ships
6 workloads, scoped to logistics & supply chain.
Concrete applications where ai & machine learning unlocks measurable value inside logistics & supply chain delivery constraints.
- 02
ETA prediction
Telemetry-grounded ETA models with explicit calibration to driver behavior, traffic, weather, and stop dwell. Integration with customer-facing tracking and exception escalation.
- 03
−12%
fuel spend
RL dispatch with override learning
Reinforcement-learning dispatch engine that learns from dispatcher overrides. Override rates typically drop from 60% to under 20% within 4 months as the engine absorbs operational knowledge.
- 04
Demand forecasting
Multi-horizon demand forecasting respecting promotion calendar, seasonal patterns, and capacity constraints. Integration with WMS and ERP for closed-loop replenishment.
- 05
Last-mile AI
Last-mile delivery optimization with proof-of-delivery vision, address-resolution AI, and driver-facing assistance, engineered against connectivity reality.
- 06
Network and capacity planning
Strategic network optimization, hub-and-spoke planning, and capacity simulation. Multi-modal where the workload spans truck, rail, ocean, air.
How we engage
4 phases, named in the SOW.
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to logistics & supply chain delivery.
- 01
Operating reality assessment
Discovery in dispatch ops, ride-alongs with drivers, audit of TMS / WMS configurations. Architecture lands against actual operations, not optimistic backtests.
- 02
Real-time architecture with exception handling
Streaming-first architecture for routing, ETA, and exception flows. Latency calibrated to dispatch decision cadence. Exception handling designed in from architecture, not bolted on.
- 03
Co-built dispatcher tooling
Dispatcher and driver UIs co-built with the people who use them. We sit in dispatch centers during design; the model that ships is the model dispatch trusts.
- 04
Operating cadence
Quarterly model recalibration against drift and seasonal shifts, monthly exception-pattern review, and the operational discipline logistics rhythms require. Most engagements continue through multiple peak seasons under Managed Services.
Capabilities
What’s in scope.
- Real-time route optimization with exception handling and TMS / WMS integration
- ETA prediction with telemetry-grounded driver-behavior calibration
- Reinforcement-learning dispatch engines with override-feedback policy update
- Demand forecasting with promotion calendar and seasonal pattern awareness
- Last-mile AI: proof-of-delivery vision, address resolution, driver assist
- Network and capacity planning with multi-modal optimization
- Streaming infrastructure: Kafka, Kinesis, event-driven workers
- Edge inference for in-cab assistance where latency demands it
Stack
Tools we use in production.
- Foundation models
- Anthropic ClaudeOpenAI GPTAWS Bedrock
- ML platform
- SageMakerVertex AIMLflowPyTorchJAX
- Streaming
- Apache KafkaAWS KinesisApache FlinkConfluent
- Routing & optimization
- OR-ToolsGurobiOptaPlannerCustom RL frameworks
- TMS / WMS
- ManhattanOracle TMSBluJayJDA / Blue Yonder
- Telemetry & maps
- GeotabSamsaraMapboxHEREGoogle Maps Platform
Compliance overlay
Frameworks we engineer to.
Every logistics & supply chain engagement carries the evidence collection that procurement and audit teams expect on day one.
- 01
DOT / FMCSA
DOT-aware design with hours-of-service compliance integrated into routing and dispatch decisions. ELD-compliant data flows; the audit-trail tooling DOT examination requires built into the platform.
- 02
C-TPAT (cross-border)
Cross-border logistics workloads with C-TPAT-aligned data handling, sanctioned-entity screening, and the audit trail Customs and Border Protection inquiries expect.
- 03
Worker privacy and labor
Driver-facing AI (telemetry, behavior monitoring, productivity analytics) engineered against driver-privacy frame, union agreements where applicable, and explicit consent / opt-out patterns.
- 04
SOC 2 Type II
Continuous evidence collection: control activity logs, change-management artifacts, access reviews. Produced as a side-effect of operations.
Selected work
Quantified outcomes, not adjectives.
Where this fits
Open the parent practice or industry hub.
Common questions
Asked before the first call.
- 01
Can you integrate with our existing TMS and WMS?
Yes: Manhattan, Oracle TMS, BluJay, JDA, and most major WMS platforms are in our active engagement portfolio. We integrate as primary scope (not phase 2), with documented interface contracts, dual-write windows for critical paths, and explicit fallback for partner unavailability.
- 02
How do you handle dispatcher trust?
Co-build, not deploy-at. Senior engineers sit in dispatch centers during design and rollout. The model surfaces its reasoning to the dispatcher; overrides feed the model's policy. Most engagements show override rates dropping from 60% to under 20% within 4 months, not because we forced trust, but because the engine absorbed the operational knowledge.
- 03
Do you do edge inference for vehicle workloads?
Yes: when latency demands it. NVIDIA Jetson, AWS Wavelength, in-cab GPU deployments. Edge for in-cab assistance, driver-behavior monitoring, and routing-decision support. We tell you when edge is the right answer (latency, connectivity) and when cloud with QoS-aware sync wins.
- 04
Can you support multi-modal logistics AI?
Yes: truck, rail, ocean, and air all have shipped engagements. Multi-modal optimization, network-flow planning, and the operational tooling carriers and 3PLs actually use.
- 05
What about driver privacy?
Engineered from architecture. Driver-facing AI (telemetry, behavior monitoring) deployed with explicit consent / opt-out patterns, union-agreement awareness, and the operational discipline labor relations require. We tell you when an AI use case is fundamentally bias-fragile or labor-relations-sensitive.
- 06
What does a logistics AI engagement cost?
Discovery: 4–6 weeks, $60K–$150K. Routing / ETA program: 6–10 months, $500K–$1.5M. RL dispatch program: 6–9 months, $400K–$1.2M. Multi-modal optimization programs: $1M–$3M+. Managed Services: $40K–$150K monthly retainer.
Talk to us
Bring us a hard AI & Machine Learning problem. We’ll bring a senior Logistics & Supply Chain engineer.
A senior engineer plus the CORTEX department lead joins the first call, both with prior logistics & supply chain delivery experience.