Haladir is the decisional AI layer for logistics. We sit on top of your WMS, TMS, OMS, etc., unify their data into one operational graph, and embed solver-grade optimization, ML models, and process intelligence into the decisions that power supply chains. Today's AI brought intelligence. The next frontier is judgement.
frameworkNomos is our framework for building AI decisional intelligence for logistics, unlocking maximally optimal decisions at breakneck speed across the entire supply chain: warehouses, trucking, air and ocean freight, and everything in between. The five stages describe Haladir’s continuously corrected decisional AI layer for logistics. Some components are interchangeable across engagements, others are scoped, built, and tuned per customer.
Data infrastructure unifies operational data across the systems you already run: WMS, TMS, OMS, YMS, ERP, the data lakehouse, etc. IDs, timestamps, and event semantics are reconciled so that SKUs, orders, shipments, lanes, pallets, and shifts become first-class objects, ready for the process intelligence stage to consume. Integrations and the unified schema are shaped to each customer’s data landscape.
Process intelligence reconstructs the operation from the events your systems produce, building a live digital twin of the warehouse floor, the lanes, and the labor schedule. Object-centric process mining tracks pallets, orders, waves, shipments, and appointments simultaneously. The digital twin and process is built and mapped around the customer’s own operation.
Models and optimization brings together ML predictions and constraint solvers. ML models predict the uncertain quantities the operation depends on (demand, dwell, ETA, no-show risk). Solvers turn those predictions plus the constraints surfaced by process intelligence into optimized decisions across vehicle routing (VRP), pick-path optimization, wave release, dock-door assignment, slotting, labor allocation, and inventory positioning. Models, solvers, and decision classes are scoped per customer implementation.
Implementations are made upon customer preference during integration. The framework supports several channels: operator review for high-stakes calls, direct push into the WMS, TMS, YMS, or OMS for well-constrained decisions, and AI agents for multi-system coordination across carriers and customers. The mix shifts over time as the framework proves itself on specific problem types.
Monitoring and observability provides live system state, decision traceability from inputs to action, and recommended-versus-executed comparison with operator overrides captured at the moment they happen. Failures are attributed back to the layer that caused them, whether data drift, workflow drift, forecast error, or solver infeasibility, so the framework continuously corrects itself. Metrics, thresholds, the improvement loop, and root-cause taxonomies are calibrated to each customer deployment.
Global and regional contract logistics providers spanning warehousing and fulfillment, cold-chain storage, air freight, ocean freight, and trucking.
Brand-owned and independent distribution networks spanning DTC fulfillment, wholesale replenishment, retail allocation, and multi-DC inventory positioning.
Beneficial cargo owners and shippers managing global freight spend, multi-mode transportation, and carrier performance across their own supply chain.
We tap the integration layers you already use across WMS, TMS, OMS, etc., feeding their data into a single operational graph. No re-onboarding partners, no rip-and-replace.
From that graph, our five-stage platform turns your operation into continuously optimized decisions, executed back through your existing systems or through AI agents acting on your behalf.
If you run a 3PL, a contract logistics operation, a distribution network, or a global supply chain, we should talk.
