The most valuable resource in a factory is not the steel or the silicon. It is the senior planner who can translate a foreman's complaint about a bottleneck into a linear constraint that a solver can understand. That person is expensive, rare, and often speaks a different language than the rest of the business. Harumi, a San Francisco-based startup, is betting that a large language model can be that translator [harumi.io, 2024].
Their pitch is a simple swap: instead of a months-long consulting project to build a custom optimization model, you describe your problem in plain English. The LLM reasons through the business logic, writes the mathematical formulation, and generates the code for a solver like Gurobi. The output is a plan that tells you how to cut material with less waste, or schedule trucks to avoid idle time, presumably for a monthly SaaS fee. It is operations research, but with the friction of modeling supposedly scraped away.
A wedge into the real economy
Harumi is not chasing the usual tech-savvy early adopters. Its stated targets are manufacturers, retailers, and logistics providers, the “real economy” companies where spreadsheets and tribal knowledge still govern billion-dollar asset utilization [harumi.io, 2024]. The wedge is specific: cutting stock optimization. In industries from apparel to aerospace, figuring out how to cut shapes from raw material with minimal waste is a classic, computationally nasty problem. Solve that one painful, quantifiable use case,reducing scrap by a few percentage points,and you have a foot in the door to tackle scheduling, pricing, and transport.
The company raised $600,000 in a pre-seed round led by Brazilian venture firm ABSeed to build out this premise [harumi.io, 2024]. The team, estimated at 11-20 employees, lists specialties in optimization and industrial engineering, a signal they understand the domain they’re automating [Prospeo, 2026]. While the founders keep a low public profile, the company has garnered recognition in its apparent secondary market, being named one of the most innovative Brazilian startups by KPMG [harumi.io, 2025].
The case for automated modeling
The potential efficiency gains are not trivial. Inefficient production planning and material waste represent a massive, slow-moving carbon cost embedded in global industry. If Harumi’s AI can consistently shave even a single percentage point off material waste for a mid-sized manufacturer, the climate math starts to pencil out quietly, without a single mention of ESG. The business case pays for itself; the emissions reduction is a byproduct.
Harumi's early positioning suggests a platform approach, with several optimization surfaces already outlined:
- Cutting stock. The flagship use case, aiming to minimize raw material waste in manufacturing [harumi.io, 2024].
- Pricing optimization. Dynamically setting prices to maximize revenue, a different flavor of constraint-based problem [harumi.io, 2024].
- Transport management. Routing and scheduling for logistics fleets [Capterra, 2026].
- Crew scheduling. Allocating human resources against shift and skill constraints [Capterra, 2026].
The solver in the room
The most immediate counterfactual is not another startup, but the entrenched incumbent: the operations research consultant. Firms like Gurobi and IBM offer powerful solvers, but they require expert modelers to wield them. The risk for Harumi is that its AI becomes a sophisticated prompt wrapper for these same solvers, never quite capturing the nuanced business context that a human expert would. If the LLM misinterprets a critical constraint,say, the mandatory cooldown time between machine runs,the “optimal” plan could be disastrously wrong. Trust, in this domain, is built over years, not through a chat interface.
Furthermore, the public traction is opaque. There are no named customers or detailed deployment case studies in the available sources. An estimated annual revenue of ~$1.37 million suggests some commercial activity, but it’s unclear if that comes from a few pilot projects or a growing SaaS base [Prospeo, 2024]. The next twelve months will be about moving from a promising tool to a relied-upon system. That means landing a flagship customer in a hard industry and proving the model’s recommendations are not just clever, but correct and reliable under real factory-floor pressure.
A back-of-the-envelope calculation: if a midsize apparel manufacturer spends $10 million annually on fabric, and Harumi’s cutting stock optimization reduces waste by a conservative 2%, that’s $200,000 saved in material costs alone, not counting reduced disposal fees. The unit economics of saving a ton of CO2 from never producing wasted material become compelling. For Harumi to matter, it must consistently deliver that kind of result and do it more reliably and cheaply than the boutique operations research consultancy it aims to displace. That is the incumbent it must beat.
Sources
- [harumi.io, 2024] Harumi - AI Powered Production Planning | https://harumi.io/
- [harumi.io, 2024] Cutting stock | Harumi | https://harumi.io/solutions/cutting-stock
- [harumi.io, 2024] Pricing optimization | Harumi | https://harumi.io/solutions/pricing-optimization
- [Prospeo, 2026] Prospeo company profile | https://prospeo.io/company/harumi
- [Capterra, 2026] Harumi profile | https://www.capterra.com/p/10000000/Harumi/
- [harumi.io, 2025] Harumi - Scale Your Consulting Projects with AI | https://harumi.io/partners