Problem

Haladir addresses the fragmentation and opacity of modern logistics operations. 3PLs and other supply-chain operators typically run multiple systems—WMS, TMS, OMS, ERP, and related tools—with inconsistent identifiers, timestamps, and event semantics. That prevents operators from seeing the business as one connected operation: downstream decisions inherit the underlying data problem, and generic SaaS often underdelivers because each logistics operation has its own workflows and constraints.

The economic pain is both operational and strategic. A company cannot reliably optimize what it cannot represent as a unified graph, while warehouse and network decisions can involve capital-intensive commitments such as automation investments, facility consolidation, and the number and location of sites. The main use case is a 3PL or contract-logistics operator that needs to coordinate decisions across warehouses and transportation—such as routing, pick paths, wave release, dock-door assignment, labor allocation, and inventory positioning—instead of optimizing each process in isolation.

Product / Service

Haladir’s Nomos framework is a decisional AI layer that sits on top of a customer’s existing logistics stack rather than replacing it. It first unifies and cleans data from systems such as WMS, TMS, OMS, YMS, and ERP into a single operational graph; it then reconstructs the operation as a live digital twin through process intelligence and object-centric process mining. Solver-grade optimization and machine-learning models use that shared representation to make decisions across the supply chain, from vehicle routing and warehouse execution to labor and inventory.

The system combines language models with deterministic optimization: the LLM translates operational rules into precise constraints and proposes tunable parameters, while the solver finds a verifiable optimal answer. Delivery is deployment-specific: some components are reusable, while integrations, schemas, metrics, thresholds, and improvement loops are calibrated to each customer. Decisions can be presented for operator review, pushed directly into WMS/TMS/YMS/OMS systems when sufficiently constrained, or coordinated through AI agents across carriers and customers, allowing Haladir to improve decisions without a rip-and-replace implementation.

Market

Haladir competes in logistics and supply-chain operations AI, particularly the emerging category of decision intelligence and operational optimization for 3PLs and contract logistics. Its adjacent competitive set includes o9’s AI-powered planning and execution platform, Kinaxis’s AI-enabled supply-chain orchestration, Blue Yonder’s warehouse and supply-chain management software, and project44’s supply-chain visibility and decision-intelligence offerings. These companies are not necessarily one-for-one substitutes: Haladir’s stated differentiation is a solver-backed layer that unifies a customer’s existing systems and reasons across operational constraints.

The company is early-stage. YC lists Haladir as founded in 2025, part of Winter 2026, active, and a four-person team; Haladir announced a $4.3 million-to-date seed round on April 29, 2026, led by BOX Group and Susa Ventures, with additional participation from Y Combinator and other investors. It says it is working with 3PLs, distributors, and frontier AI labs, but the public materials gathered here do not disclose revenue, named customers, or production deployment metrics. Accordingly, Haladir is best treated as pre-revenue or early-commercial for diligence purposes, rather than as a company with established market traction.