The context layer starter
What a context layer actually is
The businesses getting real value from AI have built something underneath it. A context layer is that thing: your brand, your customers and your standards, written down once so every tool your team opens is working from the same source.
Built once, used by everyone
A prompt lives and dies in one chat window. A context layer you write once, keep in one place, and everyone works from. It doesn't replace prompting. It makes every prompt better.
It's examples, not adjectives
Warm and confident describes your brand. It doesn't show a model how you actually write. Four things you've published and four you'd never send does, and it's the biggest single unlock we see.
Structure decides whether it works
Past a small library, a model doesn't read everything you've given it. It searches and pulls what looks relevant. So how you name and split your files decides what it finds, and what it finds decides the quality of the work.
What you will build
Six steps across 11 pages, with four prompts you can paste straight into whatever model you already use. Start with one job you do often, then build out from there.
Run the brand interview
Who you are, who buys, what you sell, and how you see the world.
Write a card for each job
One card per job, because the job changes the writing.
Collect examples, job by job
Good and bad, plus what you say and what you never say.
Sort your facts into three tiers
What you can claim safely, what needs checking, what you never claim.
Store it where the model can reach it
A project folder, a drive, or cloud storage it can search.
Write every correction back
After every session, so the layer improves instead of ageing.
Who this is for
A business owner or marketing lead already using AI who wants the output to need less editing. You don't need to be technical. You do need to know how your business talks.
Why the bad examples matter
The bad examples will do more work than the good ones. A model given only good work learns a target. A model given both learns where the edge is.
Or have us build it and run it
Some people take the starter and run with it. Others would rather hand it over. Here's what that looks like.
Book a callYour voice gets built, not just described
Your voice is easier to show than to describe. We write the good and bad examples with you, and set the rules job by job. A pitch and a complaint reply aren't the same voice.
Every use case is tested before it goes live
Customer email, job ad, tender, board pack. We tune each one against real briefs, then fix what drifts before you rely on it.
Models change, and they don't all read you the same way
The same layer reads differently on Claude, Gemini and ChatGPT. Chat tools also switch models without telling you. We test yours on the model you actually use.
We keep it current
Prices move, people leave. A layer nobody maintains quietly starts lying. We review and update yours, so what the model says about you stays true.
