Pioneering Research, Shaping Regulation and Technology
Amplifi is built on pioneering, evidence-led research developed in close collaboration with academic institutions, regulators, and industry partners.
Our Academic, Education and Regulatory Partners
Collective Intelligence
Intelligibility is a multi-disciplinary challenge. To provide a truly holistic learning experience, we collaborate with a diverse network of leaders, incuding:
Academic pioneers: world-class universities and linguistic researchers to root our methodology in cognitive science. This ensures our benchmarks for "understanding" are backed by empirical data, not just intuition.
Sector specialists: From Credit Unions to Securities and Investments, our sector partners provide the 'boots on the ground' context. They help translate abstract regulations like Consumer Duty into practical, daily workflows for your team.
Regulators: Intelligibility sits at the intersection of law, supervision, and consumer protection. In an environment shaped by the Consumer Credit Act and Consumer Duty, regulators require new tools to assess whether language supports or undermines regulatory objectives.
Much of our content, particularly that which has been developed in partnership with proffessional bodies and trade associations, is CDP accredited.
Experts in their field
We believe in a collaborative ecosystem. Amplifi Academy creates exclusive content, masterclasses, and research alongside our network of global learning partners
↓ 35%
Carbon Emissions in 18 months
Chartered Institute of Securities and Investments
The Chartered Institute for Securities & Investment (CISI) is a global professional body that sets the highest standards of professional excellence in a broad range of financial services disciplines.
The Association of British Credit Unions Limited (ABCUL) is the main trade body for credit unions in Britain. It provides info, training and development to help members grow as sustainable providers.
Development of a mathematical intelligibility framework to analyse and reduce complexity in numerical content within financial documents, particularly credit agreements.
This project identifies the primary sources of confusion and anxiety in credit card agreements through consumer experiments, analysing the factual and cognitive factors that hinder user understanding.
Development and validation of AI agents and LLM-based systems, including personas and feedback models, to analyse, simplify, and evaluate financial document intelligibility.