Announcing our $4.3M-to-date seed round to build operational superintelligence

By The Haladir Team. April 29, 2026.

Our seed round

We at Haladir just raised our seed round, bringing our total amount of funding to $4.3M to build operational superintelligence. Led by BoxGroup and Susa Ventures, with participation from Sunflower Capital, Valkyrie Ventures, XPRESS Ventures, and various angels, with prior round funding from Y Combinator and SV Angel, and our first believer, Josh Browder.

Building operational superintelligence

Superintelligence may be a hyped word, but for us it means something quite concrete in the context of logistics and supply chain. As a company, we believe that LLMs are powerful tools, but we simultaneously believe that without the proper scaffolding, they are terrible decision makers. At a fundamental level, LLMs are not intelligent. Instead, they make great educated guesses at incredible speeds.

Recognizing this, we believe LLMs are useful insofar as they augment more traditional means of modeling. SMT/SAT solvers, MILP solvers, OR tools, forecasting models, and even simple linear programming models have all gotten extraordinarily powerful in the last decade, breaking records in solver speed that were never thought possible. Instead of offloading deterministic decision processes to LLMs, as many agent-style workflows attempt, we should be using LLMs as formalization tools. The LLM translates an operation's rules into precise constraints and writes the tunable parameters that live inside each constraint; the deterministic solver does the actual work of finding the optimal answer.

After all, LLMs are language models, and nothing more, so why try to shoehorn them into decision-making? Just as LLMs are great at writing code because code is verifiable, LLMs can be great at decision-making when those decisions are bounded by constraint optimization or other traditional ML and forecasting techniques.

From data to decisions

Decisions that cut millions in losses are only possible when the model representing your operations is both accurate and flexible. Most logistics organizations have neither, not for lack of will, but because the underlying structure isn't there to support either property.

The first step is to take the distributed, often siloed data networks that a 3PL or distributor operates and unify them into a single structured representation. In practice this means targeting the WMS, TMS, and OMS first, unifying their schemas, cleaning the underlying data, and exposing it as an operational graph that flexible modeling can actually consume. Connectivity is no longer the hard part; making the data interpretable is.

The second step is formalization: encoding the operation's rules, dependencies, and constraints precisely enough that a model and a solver can both reason about them. Process mining is one component of this, working backward from clean event data to recover the real control-flow graph the operation actually follows, but it is only one piece.

Only after these two steps does the question of what to optimize for become tractable. For many of the most valuable problems, defining the objective is itself the bottleneck. A warehouse manager's job is a Pareto frontier of service level, labor cost, equipment wear, peak preparedness, and contractual obligations. Choosing the right scalarization, or the right multi-objective frontier and the right way to traverse it, is where operations research stops being a textbook exercise and starts being engineering.

Our next steps

We are working today with 3PLs, distributors, and frontier AI labs to put operational superintelligence into practice. The most obvious starting point is the existing ML stack inside a 3PL: demand forecasting, pick-path optimization, ETA prediction. Improving its error and optimality cuts measurable losses in a margin-constrained business. From there, the same modeling discipline expands across the company, informing not only predictive layers but the actual decision processes underneath them.

Every operational decision should be informed by the most accurate representation of how a business actually operates. In a logistics company today, and in any industry over time, every decision should be automated through a framework that delivers the most optimal action available at every moment. That is what earns the right to be called operational superintelligence.