You get a far more useful AI by shaping its context, not by chasing a smarter model. Here are the levers.
Ashkaan Hassan · We Solve Problems · EO Los Angeles
You open ChatGPT.
You explain your business from scratch.
You get a decent answer.
You close the tab.
Repeat tomorrow.
Sound familiar?
AI without your context is a brilliant consultant with amnesia every morning.
You do all the work bringing it up to speed. Every single time.
The model is already smart enough. The problem is that it doesn't know you.
The same limit caps your whole business. What you can do was always bounded by who you could hire. Now competent work is something you buy by the unit — but only if you can hand it your context.
The bottleneck moved.
“Can I afford to hire for this?”
“Have I written down what it needs to know?”
The newest model with no context about your business will lose to an older one that knows everything about you.
Context beats raw intelligence.
So the real question is how you give AI your context. That's the rest of this talk.
I run a small IT firm, and this system runs alongside me every day. Everything I'm about to show you is in production, wired into the real business.
of automations in production
tool I use, wired in
running unattended, on a schedule
vendor lock-in, all plain text
It runs my company and my house. None of this is a demo.
Six levers that shape its context. Each one makes the AI know you a little better.
Your AI behaves the same way every time — your way. How you communicate, what tools to reach for, what's off limits.
Same file holds your voice and standards. Everything comes out sounding like you on the first draft, so you stop editing AI output to sound like yourself.
Write it once. Every conversation after that follows it.
Output matches your tone — direct, concise, technically deep. No more editing AI drafts to sound like you.
The AI delegates research, bulk edits, and scheduled tasks to specialist agents. It manages them like you'd manage a team.
Every session follows the same lifecycle: load context, do the work, log what happened, save everything. Automatically.
The AI never sends emails without approval, never exposes credentials, never takes an action you haven't authorized.
Different AI agents can get different instruction files — a research agent gets different rules than your primary agent.
The AI always has exactly the right information for what you're doing — without you asking for it.
Writing an email? It already loaded your email templates. Talking about a client? It already pulled their profile.
It never loads everything at once. It stays fast and focused by only pulling what the moment requires.
The AI knows your world — your people, your numbers, your goals. It never asks "what do you do?"
Mention a person — the AI knows them. Discuss finances — it has your budget. Context shows up automatically.
Work happens while you sleep. Reports generate, systems get monitored, content gets drafted — all on a schedule.
The AI understands each automation and can modify, debug, and extend them on its own.
I have dozens of automations running right now — from news digests to financial reports to health tracking.
Your AI talks to all your tools — pulling data, pushing updates, and orchestrating across systems you already use.
Your AI becomes the nervous system tying all your tools together.
"What did we decide about the pricing model?" Six months later, the AI knows.
Every initiative is tracked as a project with status and next steps. Every session ends with a journal entry logging decisions and changes.
The AI's memory gets better over time — not worse.
Each initiative gets a folder with status, description, and next steps. The AI updates these as work progresses.
Daily session logs capture every decision, change, and lesson. Searchable forever. Every session compounds on the last.
The one lever that keeps the other five current. A correction becomes a rule, so your context updates itself. Three parts — miss any one and it's dead.
Did it find out how it went?
The correction you gave. The deal that closed. The alert that fired.
Did the lesson get written down?
A rule in a file. Not a chat message that scrolls away and is gone by morning.
Does the next run read it?
The rule loads before the work starts. So the mistake can't happen twice.
Signal without capture is a lesson you forget. Capture without recall is a file nobody reads. You need all three.
Not a diagram. This is a real loop from my system, running right now.
The AI asked to deploy something. I told it: a bug report is not permission to ship. Fix the tree, don't push until I say so.
That correction became a rule in a file, stamped with the date I gave it.
That rule now loads at the start of every session, before any work happens. I have never had to give that correction again.
Every rule in my system is a mistake that can't happen twice. The list only grows in one direction.
Consistent behavior, your voice
It knows your world
Work happens while you sleep
All your tools, connected
It remembers everything
It gets better on its own
All plain text. All in one folder. All version-controlled.
No database. No proprietary format. No vendor lock-in.
One orchestrator, multiple specialists
Your primary AI doesn't do everything itself. It delegates to specialists — just like you manage a team.
Each specialist has its own instruction file. The primary agent only receives a summary back — keeping it fast.
Every session builds on every session before it. That's the compounding effect.
What this system produces in practice
Every Monday, 45 minutes pulling ticket data, calculating KPIs, typing numbers into a spreadsheet.
Ticket system → AI computes KPIs → pushes to scorecard. Every Monday at midnight. Zero human effort.
Every day at 7:00 AM, an email lands in my inbox:
I didn't ask for it. It just shows up.
Dozens of automations running 24/7. When something fails:
A monitoring system catches it immediately
AI diagnoses the root cause
Applies the fix automatically
Notifies me only if it can't self-resolve
Most failures are fixed before I even notice.
This isn't incremental improvement. This is a step change.
And it compounds — every automation you build frees time to build the next one.
How to start shaping context this week
You don't build all six levers at once. You pull the first two, and add the third so it keeps improving.
Who you are, how you communicate, what's off limits, what your voice sounds like. Save it where your AI reads it. Lever one, done.
Your team, your key clients, your quarterly goals. A few files is enough to feel the difference. That's lever two.
Every time you fix the AI, add the lesson as one line. That's the feedback loop — your context now updates itself instead of drifting.
You re-explain yourself every session. The AI knows nothing about you.
It knows your business, your voice, your world. The output already sounds like you.
It corrects itself. Your context updates every time you use it, and gets better without you.
Getting to Shaping takes an afternoon. Compounding is the habit after that.
I open-sourced the framework behind everything in this talk. It's called Contextium.
Apache 2.0. Free forever. You own everything.
$ curl -sSL contextium.ai/install | bash One command. Five minutes. The floor is already built.
Open floor
Ashkaan Hassan
We Solve Problems · wesolve.tech · EO Los Angeles
contextium.ai — open source, free forever