01
Inference engine
Upload five pieces of your best work. In 30 minutes, the system analyses how you think, communicate, and operate, then presents what it found for your review.
Membership
Start your Learned Context membership
Professional calibration system: inference engine, conductor with 4 modules, compounding profile, and all updates.
$499per year
Setup link sent immediately after payment. Access your calibration files right away.
What you get
Concrete artefacts, not vague access.
The membership gives you a working calibration system: files, operating logic, and the setup path that makes AI start from your standards instead of generic defaults.
02
Conductor
An intelligence layer that teaches your AI how to use your profile for real tasks: meeting synthesis, decision briefs, daily prioritisation, and weekly reviews.
03
Professional profile
A structured profile you own as a plain text file. Works in Claude, ChatGPT, Perplexity. Take it wherever you work.
After setup
What changes once it is live.
Less re-briefing
You stop rewriting your role, standards, and context at the start of every meaningful session.
Sharper output
Recommendations reflect your actual tradeoffs, not the safest generic answer the model can produce.
Portable profile
Your profile is a plain text file you own. It works wherever you work, today and next year.
Questions
Straight answers before you subscribe.
What exactly am I paying for?
The inference methodology that produces a profile no generic prompt can match, plus the compounding system that deepens it automatically. The files are the delivery mechanism. The intelligence is the product.
Is this another AI tool?
No. Learned Context calibrates your existing AI tools to how you think. It improves the starting conditions rather than replacing the model. No new app. No integrations. Two files.
Isn't this just prompt engineering?
Prompt engineering tells the AI what to do. Learned Context tells the AI who you are. A prompt says 'write a concise email.' A professional profile says 'this person opens with the conclusion, shortens sentences by 40% for senior audiences, and never buries the ask past the second paragraph.' The output difference is the difference between a template and a tailored suit.
My AI already has memory. Why do I need this?
Platform memory collects facts: 'user works in finance,' 'user prefers bullet points.' Learned Context structures your reasoning: how you evaluate, prioritise, and decide. The depth difference is the difference between someone remembering your name and someone understanding your judgement.
I can do this myself. Just upload docs to Claude.
You can. Try it. Upload your five best documents and ask Claude to build a detailed profile of how you think and work. Compare the result to what Learned Context produces. The gap is in what gets extracted, how it's structured, and whether it actually changes output quality.
Will this work if I switch tools later?
Take your profile with you. It's a plain text file. Paste it into your new tool's settings. Your reasoning, voice patterns, and expertise transfer in seconds.
Should I start with the audit first?
Start with the audit if you want to diagnose the size of the gap first. Join the membership if you already know you want the full system.
Structure your judgement once. Use it everywhere.
Join the membership if you want the full system. Run the audit first if you want to quantify the gap before you subscribe.