I advise leaders on AI, review the AI tools your team has built as an independent AI architect, and step in as fractional AI CTO when you need one. Investors and acquirers: AI due diligence before the money moves.
Cambridge NLP PhD, Oxford maths and computer science, former Head of Applied AI Research, €50m+ in AI value identified at IBM, fractional CTO taking an AI product from concept to public beta, and recruiting its technical team (five hires).
The Agathon Briefing
A regular note with two or three thoughts on seeing the whole AI system clearly, people included, from someone who assesses those systems for a living.
Leaders handed an AI decision they need to stand behind, and anyone about to commit serious money to AI who needs to know whether the plan is real first.
Accountable for an AI plan or a vendor choice that has to stand up to scrutiny.
About to approve a significant AI budget, and wanting an independent technical assessment first.
Pricing a deal, with a term sheet, an LOI or an investment committee asking questions the data room can't answer.
It's not a fit if you want sign-off on a decision that's already made.
There are three ways to work with me. The difference is how many of the technical decisions I make. I lead each one myself, and I can usually start within two weeks of you saying yes.
Leadership advisory
We start with a stocktake of the AI you already use, which takes two to three weeks. Then I write a short plan with your leadership, with owners and dates, over two to four weeks. After that, I advise for 3-12 months while the plan runs, with a call whenever a decision comes up.
How leadership advisory worksIndependent AI architect
A second pair of eyes on the AI tool you've built, before more people rely on it. It starts with a one-week architecture and readiness review for a fixed fee, which tells you in a written report whether it is ready and what to fix first.
How the architect review worksFractional AI CTO
I join your leadership team part-time, typically one to three days a week, and make the technical decisions myself, on the roadmap and the architecture. The minimum term is three months.
How fractional AI CTO worksEach one is set up to end. In advisory, my part drops to a periodic check as your leadership takes these calls on. If the fixes are small (a setting, permission, prompt, log or person checking), the architect review is all you need. As fractional CTO, I help you hire the full-time CTO who takes over.
Research-level depth, and no stake in the answer.

Who you work with
I hold a PhD in Natural Language Processing from Cambridge and read Mathematics and Computer Science at Oxford. My technical work in AI spans IBM, PA Consulting and two VC-backed AI scale-ups, and predates LLMs by a decade. A former Head of Applied AI Research, I identified over €50m in AI value at IBM across telecoms, financial services and automotive. My recent work includes an AI strategy for the communications division of a major global philanthropic foundation, and 20 months as fractional CTO taking an AI product from concept to public beta.
Read my storyAn adviser who profits from the fix has a reason to find one. I don't sell builds or developers, and I never build the fix for something I've assessed. I have no vendor affiliation, and I take no fee or commission from developers.
When you engage Agathon, you work with me. I don't sell then disappear. I don't hand you off to juniors. The person on the strategy call is the same person doing the work.
Strategy, advisory and fractional CTO engagements, from an AI strategy for the communications division of a global foundation to 20 months as a fractional CTO.

Case study
How we took a ghostwriting firm from copy-pasting into ChatGPT to a patent-pending AI product in beta with enterprise users.
20 months
Engagement duration
5 hires
Team recruited
85%
AI rendering speedup

Case study
How we helped a large communications division turn AI momentum into an implementation plan — in six weeks.
6 weeks
Engagement duration
11
Stakeholder interviews
2 delivered
Technical blueprints

Case study
How we turned fragmented AI experimentation into shared frameworks, tested playbooks, and a concrete product roadmap over two months.
2 months
Engagement duration
3 half-day
Workshops delivered
Full team
Team members trained
Advisory on a joined-up strategy for AI and people, an independent read on AI tools your team built, and part-time technical leadership as your fractional AI CTO. For investors and acquirers, there's independent AI due diligence as a separate service. I lead each engagement myself, from first conversation to final deliverable.
Independent judgement on a joined-up strategy for your AI and your people.
Learn moreAn independent read on the AI tool, agent or app your team built with AI tools, once people rely on it. I don't build, fix or maintain it.
Learn morePart-time AI CTO or Head of AI with decision authority, typically one to three days a week, with the days capped in writing.
Learn moreFor investors and acquirers
Independent technical assessment for investors and acquirers of whether the AI is worth the price, and whether the advantage will last. Before you commit capital.
Learn moreIncluding briefings and sessions for deal teams, boards and leadership teams
Sessions for deal teams, boards and leadership teams, built around the AI decisions in front of them.

Technical and legal diligence are both scoped to stop short of the AI claim that underpins the valuation, so testing that claim needs a third, independent workstream.

Before signing an AI vendor contract, replace trust in the rehearsed demo with written questions about the system's workings, data, dependencies and exit terms, scored for specificity.

Big 4 firms sell scale, insurance and reassurance while independent advisors sell unconflicted senior judgement, and since AI due diligence is essentially the purchase of scepticism, the deciding factor is not the advisor's intelligence but their incentives.

Cheap generation made working prototypes trivial; knowing what deserves to be built remains the only job that matters.

A clarifying primer that recasts "sovereign AI" not as a slogan but as control across four layers - data, compute, weights, governance - and shows that only a small, identifiable minority of enterprise workloads genuinely need more than the data layer existing tools already provide.

Sovereign AI governance fails not at the framework level but in the gap between documented controls and operational reality, where jurisdictional contradictions, supply chain dependencies, and untested incident response processes accumulate risk that compliance paperwork cannot see.

Deploying Microsoft Copilot is a logistics problem; extracting value from it is an organisational one, and most companies confuse finishing the first for starting the second.
Whether there's a deal to assess, a vendor to evaluate or an AI plan you're accountable for, the first step is a conversation. I'll tell you quickly if it's not a fit.