Intercompany.io works with large multinational groups — multi-entity structures managing liquidity across a dozen currencies and as many jurisdictions. The brief is usually the same shape: liquidity and FX exposure that are tracked manually rather than automated, a cash pool structure that hasn't kept pace with how the group actually operates, or forecasting still built in spreadsheets when the data exists to model it properly. That's where traditional treasury advisory, AI-driven forecasting, and — for groups ready for it — Web3 treasury work all start from the same practitioner-led lens.
I've spent over a decade inside corporate treasury and banking, mostly on the side of large multinational groups — dozens of entities, multiple currencies, and cash pooling structures that had outgrown their original design. Intercompany.io exists because that work is usually easier with someone who isn't also trying to sell the client a facility.
My analytical approach is grounded in academic research — my Master's Thesis modelled cash pooling simulation and efficiency from both the corporate and commercial banking perspectives. That same modelling discipline led me to build CashPoolModel, an independent tool that quantifies what a cash pool is worth before the negotiation ever starts.
Today that same lens extends to two newer frontiers: applying machine learning to cash flow forecasting, and helping treasury teams think clearly about stablecoins and on-chain liquidity — without the hype.
No product to sell. No institution to protect.
Most treasury advice comes bundled with something to sell — a facility, a platform, a relationship to preserve. Intercompany.io has no balance sheet of its own and doesn't distribute anyone's product. The recommendation you get is the one the numbers support, not the one that closes a deal.