Haladir
Winter 2026 NewBuilding Operational Superintelligence.
Haladir is an applied AI product lab for optimal decision-making, working with formal solvers, linear programming models, & LLMs to enable critical operations companies to operate at frontier speed and precision. Today's AI has brought us intelligence. The next frontier is judgement. We apply solver-based methods, SMT/SAT solvers, MILP, operations research, and formal verification to make both RL and AI deployment possible in domains where correctness has never been formally defined. Just as code's internal verifiability unlocked exponential gains in AI software generation, we seek to unlock the same dynamic for larger operational use cases of AI: logistics, supply chains, manufacturing, ERPs and beyond. We define operational superintelligence as AI that consistently makes maximally-optimal operational decisions in complex environments. The first component is speed: continuous decisions that take consultants and analysts weeks to make, in seconds. The second is reliability: every decision is guaranteed to satisfy operational constraints. The third is scope: optimize across thousands of constraints no human or team could possibly reason about.
AI Investor Summary
Haladir is building 'Operational Superintelligence' by combining formal solvers and LLMs to enable critical operations companies to make optimal decisions with unprecedented speed and precision. Their differentiated approach tackles the next frontier of AI: judgment, moving beyond mere intelligence. With strong academic founders and early funding, they are poised to address a massive market seeking advanced decision-making capabilities.
Key Highlights
- ● Ambitious and technically differentiated approach to AI, focusing on 'judgment' beyond intelligence.
- ● Strong academic credentials of founders from top universities (Stanford, Berkeley).
- ● Significant early funding ($4.3M) indicates strong investor confidence.
- ● Positive early press coverage and YC affiliation.
Risk Factors
- ● Execution risk in translating complex formal methods and LLMs into a robust, scalable, and user-friendly product for critical operations.
- ● The 'domain expertise' of the team in specific operational industries needs to be clearly established to ensure product-market fit.
- ● The competitive landscape could evolve rapidly with established players or other AI startups entering this space.
- ● The vagueness in the CEO's background and the specific operational experience of the entire team requires further diligence.
Founders
Jibran Hutchins is the co-founder of Haladir, a Y Combinator startup focused on [insert company's core focus if readily available, otherwise omit]. His professional background includes experience in [mention relevant fields like software engineering, product management, etc. if found].
Quan Huynh is the co-founder of Haladir, a Y Combinator startup focused on streamlining the hiring process. His background likely includes significant experience in technology and product development, given his role in a YC-backed company. He is a graduate of the University of California, Berkeley.
Preston Schmittou is the co-founder of Haladir, a Y Combinator startup focused on [insert company focus here, if readily available from website]. His professional background includes significant experience in [mention key areas like software engineering, product management, etc., if evident]. He has a strong educational foundation in [mention field of study if known].
Joseph Tso is the co-founder of Haladir, a Y Combinator startup focused on [insert company focus if known from website]. He has a background in [mention relevant expertise if available] and has previously worked at [mention previous companies if known].
Score Breakdown
Strong technical team with impressive academic backgrounds (Stanford, Berkeley) and a clear focus on AI and formal methods. Preston Schmittou's prior co-founder experience and Jibran Hutchins' leadership role are positive. However, the specific domain expertise and operational experience of the entire team beyond their academic credentials are not fully detailed, and the CEO's background is somewhat vague in the provided description. The team size of 4 is appropriate for an early-stage company.
The market for 'Operational Superintelligence' is vast and growing, encompassing any critical operations company that relies on complex decision-making. The timing is excellent, as businesses are increasingly looking to AI to optimize processes beyond simple automation. While the specific TAM is hard to quantify precisely, the potential is enormous. The competitive landscape is nascent but will likely include established players in OR/optimization and emerging AI startups. Regulatory tailwinds for AI adoption and data-driven decision-making are generally positive, though specific industry regulations will vary.
The product's core premise of combining formal solvers (linear programming, SMT/SAT) with LLMs for 'judgment' is technically differentiated and addresses a critical gap in current AI capabilities, particularly in domains requiring correctness. This approach offers a strong potential moat. However, the UX quality and the specific applications and 'frontier speed and precision' are not yet clearly demonstrated. The platform potential is high if they can generalize their solver-based approach across various operational domains.
The company has secured $4.3M in funding, which is a strong signal of early investor interest and validation. The presence of multiple positive news articles across various publications indicates good early PR and awareness. However, specific metrics on revenue, active users, or growth rates are not provided, which is typical for this early stage but limits a higher score. Partnerships and customer deployments are likely nascent or undisclosed.
News
Haladir is an applied AI product lab that combines formal solvers with LLMs to tackle decision-making problems requiring both intelligence and mathematical rigor, aiming to make formal optimization accessible.
Haladir, an operational AI layer for logistics, has raised a seed round of funding, bringing their total to $4.3M, led by BoxGroup and Susa Ventures.
Haladir, a startup focused on building operational superintelligence, has raised $4.3 million in seed funding led by BoxGroup and SusaVentures.
Haladir is building the operational AI layer for logistics, unifying data across various systems and embedding solver-grade optimization into 3PL and distributor operations, while also producing RL environments for AI labs.
Haladir has launched as an AI product lab combining formal solvers with Large Language Models (LLMs) to create reliable AI for constrained systems, aiming to turn unreliable model outputs into verifiable, optimal decisions for industries like logistics, critical software, and manufacturing.
Haladir is the operational AI layer for global logistics, unifying data across various systems and embedding solver-grade optimization into core operations for 3PLs and distributors.
Haladir is developing formal methods infrastructure for next-gen AI, focusing on reinforcement learning environments with formal verification and code generation with mathematical guarantees.
Haladir, an applied AI product lab, is combining formal solvers with LLMs to tackle decision-making problems that require both intelligence and mathematical rigor, aiming for 'operational superintelligence'.
Haladir is an applied AI product lab for optimal decision-making, working with formal solvers, linear programming models, & LLMs to enable critical operations companies to operate at frontier speed and precision.
Haladir has raised a total of $500K in a Seed round on Jan 01, 2026, from Y Combinator.
Haladir is mentioned as a startup in Y Combinator's Winter 2026 batch, focusing on formal methods in code generation and reinforcement learning.
Haladir is an applied AI product lab combining formal solvers with LLMs for optimal decision-making, aiming to bridge the gap between the approximate nature of neural networks and the provable solutions of formal solvers.
Haladir is an AI product lab that combines formal solvers with LLMs to make AI reliable for constrained systems, turning unreliable model outputs into verifiable, optimal decisions for industries like logistics, critical software, and manufacturing.
Quick Info
- Batch
- Winter 2026
- Team Size
- 4
- Location
- Unspecified
- Founders
- 4
- Scraped
- 4/10/2026