We Trade Bobby Cars for Ferraris.
A pure mathematical optimizer is like a Ferrari without a driver — very powerful, but aimless.
Human operators are expert drivers stuck in a bobby car — full of experience, but slowed down by manual work.
Arqh puts expert operators in the driver's seat of a high-performance machine — giving them instant, precise control over powerful optimization tools that amplify their expertise and accelerate execution.
An Obsession Born From Frustration.
After months sitting side-by-side with fuel dispatchers and airline crew planners, we were stunned. Mission-critical decisions affecting millions in revenue hinged on colour-coded spreadsheets, frantic phone calls, and fragile tribal knowledge.
That experience lit the fuse for Arqh. We're building a system that thinks at solver speed but speaks in human terms—so the experts who keep the world moving finally get the tools they deserve.
The Team
CEO & Co-founder
Computer Science from ETH Zurich. Former president of tech consultancy ETH juniors and member of the high-performance computing team.
An entrepreneur since 15, from running a ginger-shot business to deep-tech consulting.
CTO & Co-founder
MSc in Data Science from ETH Zurich. Machine intelligence researcher at ETH & EPFL, former engineer at Deloitte.
Theoretical AI PhD drop-off after 6 months to pursue real-world impact in business.
Product Manager
MSc in Machine Intelligence from ETH Zurich & researcher at Yale.
Experience in tech strategy at Deutsche Bank, PwC and Accenture.
Senior Software Engineer
MSc in Computer Science from NTUU with extensive software development experience across manufacturing and sales industries.
Expert in building robust applications for GenAI and complex data analytics solutions.
From the Lab to the Loading Dock.
Our methodologies are academically-grounded and validated by real-world application. We believe in transparency and advancing the state of the art through collaboration with leading research institutions.
ETH AI Center - Data Science Lab
Learning to Optimize for Petrol Station Replenishment
This work, implemented by ETH students Hanno Hiss and Hannes Büchi and supervised by Arqh, proposes a hybrid reinforcement learning framework to solve complex, real-world Vehicle Routing Problems.
M.Sc. Thesis - EPFL
Adaptive Heuristics for Petrol Station Replenishment
Supervised by Prof. Dr. D. Kuhn and Arqh co-founders, this thesis by Alessandro Dalbesio forms a core part of our engine's foundation, exploring adaptive methods for extended planning horizons.
Ready to Optimize?
See how Arqh can transform your operations. Get in touch with our team to start a trial today.