Service 02

Turn AI into a real lever

Your team already uses AI. The question is no longer whether to adopt it, but how to get more out of it than a line on an invoice. Six topics, taken separately or as a programme, to move from incidental use to use that counts.

01

Token optimisation

The token did not exist a few years ago; it has become what everything turns on. It is the unit by which knowledge and intelligence are measured and commoditised — the direct expression of the energy your teams burn the moment they call on AI. Managed badly, tokens are expensive; but the real cost lies elsewhere. A saturated context makes the model drift, produces hallucinations, and costs you far more in time than in money. I have worked with AI under cost constraints from the start: I pass on what that taught me, and the techniques the best AI engineers use, so that every token earns its place.

What's covered

  • What a task actually consumes, and how to measure it
  • Why a saturated context makes the model drift
  • Cutting consumption without losing any quality
  • The techniques AI engineers actually use

What you get out of it

A cost you control, and answers that stay accurate because the model gets what it needs — no more, no less.

02

Harnesses and agent teams

Most people use only a fraction of what agentic AI can do. Do not stop at the chat window: AI can plan, orchestrate, and put whole teams of agents to work on your tasks. What remains is knowing where to draw the lines — which steps demand human judgement, which ones can be handed over safely, which tools you put in an agent's hands, and what guardrails keep you in control of what your systems produce.

What's covered

  • Beyond the chat window: planning, orchestrating, delegating
  • Which steps demand human judgement
  • Which tools an agent may hold, and which never
  • The guardrails that keep you in control

What you get out of it

Work that moves without you, and a clear line between what the machine executes and what you decide.

03

Identify the right models

AI is advancing exponentially: some speak of a Moore's law for artificial intelligence, but on a radically shorter timescale. Models now multiply faster than anyone can track, and most users lock themselves into a single provider, a single family. I have put many of them to the test, and learnt to make them work together. Learn to recognise the right model for each task: that is where the gap opens between average use and use that genuinely exploits this diversity.

What's covered

  • Breaking the single-provider reflex
  • Recognising the right model for each task
  • Getting several models to work together
  • Revisiting that choice on every release, without rewriting everything

What you get out of it

The right model in the right place, and a method for redoing that call when the landscape shifts — which is to say, often.

04

Automate

Developing with AI alongside you is already a considerable gain. But it can also work while you are away: watching, reviewing, checking your code, and warning you before the problem finds you. We go through what can be grafted onto your existing pipeline without rebuilding it, and what each piece actually buys you.

What's covered

  • Monitoring, reviewing and checking code automatically
  • What grafts on without rebuilding your existing pipeline
  • What each piece actually buys you

What you get out of it

A pipeline that works while you are away, and warns you before the problem finds you.

05

Security

Handing tools to an agent also hands it an attack surface. New classes of vulnerability are appearing, specific to AI, and they look nothing like the ones your teams already know how to spot. I show you the ones emerging now, how they actually work, and which tools exist to guard against them.

What's covered

  • What a tool-equipped agent really exposes
  • AI-specific vulnerabilities, and why they resemble nothing you know
  • The tools that exist to guard against them

What you get out of it

A clear read on the risk you take when you arm your agents, and what to do about it.

06

Keeping up

No technology has ever moved this fast. The risk is not missing an announcement, it is drowning in noise: everyone comments, very few add anything. I give you the list of people and companies genuinely worth following — the ones I read myself — so you stay current without spending your days on it.

What's covered

  • Who to follow, and why them
  • Telling an announcement apart from real change
  • Keeping watch without spending your days on it

What you get out of it

A short, reliable list, and the habit of sorting signal from noise.

A one-hour conversation is enough to work out which topics apply to you.

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