Automating Complexity
Delivering Clarity For a
Sustainable Future.

Arnaud spent his career on the operator’s side of the invoices freya now audits. He knows what a freight bill costs to produce, why it arrives in the shape it does, and how rarely anyone checks it, because for most of his career he was the one sending them. His conviction is that the industry’s cost problem was never a shortage of data. It was that nobody could read it fast enough to act while it still mattered.

His career has been entirely in Asia Pacific. He arrived in Hong Kong in 1997, spent two decades in Japan, and has been based in Singapore since 2019. As President and Managing Director of Bolloré Logistics Japan he grew the business from 50 to 120 people with consistent double-digit growth, owned the full P&L, led the SDV–Saga merger, opened the Haneda airport office and ran the business-continuity plan through the 2011 Fukushima earthquake. He then took Asia Pacific sales and marketing for the group: 19 countries, 450 salespeople, a €1.5 billion budget, led remotely through COVID. Earlier he turned A. Hartrodt Japan from loss-making to profitable, with sales up 60 percent.

At TetriXX AI, which he co-founded in 2021 on his own idea and paired with Emilie’s engineering, he is deliberately not a hands-off CEO: he learned to code with AI assistance and built sirius, the weekly freight cost pressure report, and the Sirius Index behind it. He is direct about what that proves and what it does not, the tooling collapsed the distance between an idea and a working thing, but it did not remove the need for people who have carried a P&L to decide which things are worth building. His thesis is that the C-suite has been asked to run its largest uncontrolled cost line on quarterly summaries, and that it no longer has to.

Emilie is building freya, the intelligence layer for transport and logistics. A transportation invoice is treated as a document to pay and file, when it is the most complete record a company holds of how its supply chain actually runs. In most companies that data sits unused in scattered spreadsheets. It stays there because every carrier bills in a different format, none of it arrives structured, and normalising it across a whole network is harder than it looks. She is turning it into a strategic asset.

She trained as an applied mathematician at ENSIMAG in France, then spent more than fifteen years building risk, trading and data systems for global financial institutions across Europe and Asia, including ING, DBS, UOB and ANZ. Her first instinct was always the same: if the data is wrong, every conclusion built on top of it is wrong. In capital markets a 0.01 percent error triggers an alarm, so rigor, traceability and data integrity were never best practices, they were the price of entry. Supply chain has never been held to that standard.

At TetriXX AI she treats data as the product: one normalised model of what a company actually ships and pays, from which verification, cost intelligence and a forward view all follow. She runs the company AI-first as an operating model rather than as a feature, with multi-agent systems producing in minutes what used to take a team a week. She is equally direct about the limits: agents do not erase the architecture, integration, security and compliance that a Fortune 500 deployment demands, they raise the ceiling, not the floor. Her thesis is that transportation and logistics, the layer every physical industry depends on, holds more unexploited value in its own data than anywhere else in the enterprise.