Companies
13 companies. Freight forwarding, warehouse intelligence, warehouse robotics, supply chain visibility, digital freight, and fleet operations — each one built ML-first.
Distributed fulfillment network that matches merchant inventory to fulfillment nodes based on proximity, capacity, and service-level constraints — letting mid-market e-commerce brands achieve 2-day delivery without owning a single warehouse.
Predictive ETA platform for ocean freight. Ingests vessel position data, port congestion signals, and carrier schedules to produce probabilistic arrival forecasts — replacing the static ETD/ETA fields that forwarders have relied on for decades.
Shipment visibility and customer portal that freight forwarders deploy under their own brand. Forwarders stop losing accounts to brokers with better tracking UX — their shipper customers get real-time milestone updates and exception alerts without the forwarder building software.
Digital freight forwarding platform for SME importers and exporters. Rate comparison and carrier selection backed by historical lane performance data, compressing procurement cycle times that previously required broker relationships and manual RFQ rounds.
Shipping decision engine that evaluates carrier selection, service-level options, and zone-skipping logic at checkout latency — making the call that a human logistics manager would make in an afternoon, in milliseconds per order.
Load planning engine for full-truckload carriers. Uses reinforcement learning to make driver dispatch and load assignment decisions that account for regulatory hours-of-service constraints, repositioning costs, and backhaul opportunities — replacing the planner who ran these calls from a whiteboard.
Shared execution layer for shippers, carriers, and brokers. When a load misses a pickup window or a driver goes dark, all parties see the exception at the same time and can act from the same record — reducing the phone-tag that defines most freight exception management today.
Decision engineering platform for routing, scheduling, and allocation problems in supply chains. Lets engineering teams build, run A/B tests against, and deploy decision models without requiring an in-house operations research function — the OR talent gap that blocks most mid-market logistics operators.
AI brain for warehouse robots that enables robotic arms to pick virtually any item in unstructured environments — transferring learned dexterity across deployments so each new robot installation starts with the accumulated knowledge of every prior one.
Real-time supply chain visibility platform that tracks shipments across road, rail, ocean, and air in a single network — turning fragmented carrier and ELD data into predictive ETAs and exception alerts that procurement and operations teams can act on before a delay becomes a disruption.
Computer vision monitoring layer for material recovery facilities. Classifies waste stream composition in real time on conveyor belts, giving sorting operators the throughput and contamination data they need to optimize recovery rates and comply with EU packaging regulation reporting.
Cognition layer that runs above an existing WMS. Applies ML to slotting decisions, pick-path optimization, and inbound flow prediction — improving throughput and labor utilization without requiring operators to rip out the legacy system they've spent years configuring.
Full-service digital freight forwarder that replaces the email-and-spreadsheet broker relationship with a software platform giving importers and exporters end-to-end visibility, document automation, and data-driven routing decisions across ocean, air, and ground.