Tokenovate, a UK fintech firm, has appointed Alec Burns as its head of product technology, where he will strengthen product strategy, scale institutional deployment, and advance the company’s technology roadmap.
His remit also includes product experience and delivery, combining Tokenovate’s capabilities across digital legal agreements, the operationalisation of the open-source capital markets framework FINOS Common Domain Model, and programmable settlement.
Burns will help mould the integration of AI into the company’s product offering and long-term plans, centred on configuring, testing, exception management, and operational insight.
Contractual terms and defined lifecycle events will remain authoritative, the company says.
According to Tokenovate, these capacities support its “legal-first” approach to post-trade automation, which utilises the firm’s post-trade orchestration layer, Novat, at its core to represent contractual rights and obligations as settlement states.
Burns brings over 10 years of experience to his new position, spanning AI, blockchain, robotics, and product development.
Prior to the move, he built VeChain’s AI product organisation and has advised institutions on AI strategy and delivery through Metis Point, his advisory practice.
Burns holds a PhD in robotics and autonomous systems from the University of Liverpool, beginning his career as a research fellow in robotics and AI at University College London.
Commenting on the new appointee, Richard Baker, CEO and founder of Tokenovate, says: “Tokenisation is changing market infrastructure, while legal certainty, common semantics and deterministic processing remain essential.
“Our focus is on giving institutions a practical way to automate post-trade processes while working across existing and emerging infrastructure.”
Burns adds: “Tokenovate’s legal-first architecture keeps contractual intent, economic state, and settlement outcomes synchronised across systems.
“My focus is to make that architecture composable, straightforward to deploy and ready for institutional scale, while applying AI and distributed technology with engineering discipline.”
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Image: Tokenovate 