Founder · Builder · Linguist
Building AI products. Designing multi-agent systems. Studying the architecture of human language.
About
I'm a founder, product builder, polyglot and linguist. I have Master's degrees in linguistics, philology and phonetics from Oxford and in entrepreneurship from Cambridge, both with Distinction. I build AI products and multi-agent systems - connecting models to tools, data and real workflows so they can plan, act and get things done, not just answer questions. I also build with agents: coding agents and MCP-connected tooling are how my products get made. I advise others building the same way. My entrepreneurial experience spans technology, language and consumer goods.
I'm interested in learning and skill acquisition, with a particular focus on language learning. I speak nine languages. My approach is empirical: I synthesise existing research with practical experience to model how people acquire language.
Areas of Focus
Ventures
An AI-powered language learning platform built on the comprehensible input hypothesis and personalised multimodal learning. Letera took an empirical, research-driven approach to language acquisition.
Finalists at Oxbridge AI. Supported by Accelerate Cambridge and UCL EdTech Labs. Partnerships at the University of Cambridge and the University of Oxford.
An ethical consumer goods brand bringing organic and sustainable products to premium UK and international retail. Stocked at Ocado, Selfridges, Fortnum & Mason, and TJX.
Featured by BBC and Vogue. Built from first principles - sourcing, brand, distribution - demonstrating that ethical production and commercial success are not in conflict.
Languages
I hold a Master's degree in Linguistics from the University of Oxford, where my research focused on syntax, psycholinguistics and the structure of language. My interests in language centre on human language acquisition and the cognitive mechanisms that underpin how we learn and internalise new languages.
I speak nine languages, and that experience is inseparable from my research. My approach follows usage-based, input-driven models of acquisition - the view that language is learned through meaningful exposure at the right level of difficulty, with explicit instruction in a supporting rather than a leading role.
The success of deep learning and large language models offers a striking validation of this view. LLMs acquire language not through explicit rules, but through massive exposure to high-quality input - converging on probabilistic representations of grammar, meaning and usage. This mirrors what usage-based and statistical-learning accounts have long argued about human learners: that quality input, at the right level of comprehensibility, is the primary driver of acquisition. These insights inform both my research and the language tools I build.
Academic
Writing
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