Clients of large professional services firms pay a premium for access to their advisor’s in-house knowledge. However, this knowledge is often siloed across teams, making it difficult for advisors to leverage prior experience efficiently.
Revax solves this problem for professional service firms with its custom large language model and retrieval augmented generation (“RAG”) system, automating the report drafting process while increasing the fluidity of in-house knowledge.
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Founding team
Alexander Bartlam
I am currently an MPhil in Technology Policy student at Cambridge Judge Business School, with a background that blends leadership with technical expertise. I hold an undergraduate degree in Electrical and Electronic Engineering, where my final project focused on AI applications in the workplace. I complemented this with an internship in cybersecurity in New York, followed by an internship in tax.
I then joined the British Army, spending a year at Sandhurst developing leadership and management skills before taking on roles leading a technical product for the Cabinet Office and overseeing ceremonial communications in London. I finished my service as a Captain, managing communication and information systems for the UK-led mission training the Ukrainian Armed Forces. This included driving the delivery of Starlink training to Ukrainian soldiers and managing mission-critical, highly sensitive data pipelines.
Upon leaving the Army, I knew I wanted to work at the intersection of business and technology. To build on this, I interned in tech-sector M&A and fundraising advisory before starting my MPhil in Technology Policy where my goal is to gain a holistic understanding of technology trends while leveraging Cambridge’s unrivalled entrepreneurial ecosystem as a platform for a startup.
Jack Mander
I am a Chartered Accountant and Chartered Tax Advisor with a background in M&A tax advisory, specialising in private equity transactions. At Deloitte, I managed teams across buy-side and sell-side engagements, leading deal execution and financial modelling for large-cap transactions. Prior to this, at PwC, I developed experience in tax structuring, working on funds flow projects and tax valuation modelling. Before M&A tax, I interned at HSBC and held a technical scholarship with the British Army, where I trained as a University Scholar.
Beyond tax advisory, I have a strong technical interest in AI and automation. I have been fine-tuning large language models and integrating retrieval-augmented generation (RAG) techniques to improve information retrieval and report drafting. This shared interest has led to us focusing on automating professional services report writing, streamlining workflows for tax, legal, and financial advisory firms.
My experience in professional services has given me an insight into how inefficient report drafting can be, and we see a clear opportunity to use AI to drive efficiency in this space.
The Cambridge ecosystem presents an ideal environment to refine and scale this vision, providing access to research and industry networks to build a transformative solution for professional advisors.