Six months ago, we didn't know what a context window was.
Today we build systems that work around one.
Our AI journey started the way most do — with enthusiasm, a free ChatGPT account, and no idea what we were getting into. We moved through Google Gemini, got stuck in Mistral, and eventually landed on Claude, which is where things got serious.
And like any serious relationship, it came with serious frustrations.
Claude is brilliant. Claude also forgets everything the moment a conversation ends. It will confidently ignore the rules you spent three hours writing. It will overwrite a working config file with a broken one faster than you can say "did you back that up?"
Every day brought a new flavor of the same problem: AI is powerful but stateless, and stateless tools lose your work.
So we stopped building things with AI and started building things for it. Every time Claude hit a wall — the context window expired, the instructions drifted, the filesystem got clobbered — we engineered a fix and made it permanent.
Hundreds of hours later, we had something we didn't expect: a complete deployment infrastructure that lives on Git, connects to local file systems, and gives Claude the long-term memory and structured workflow it was never designed to have.
"We call it Cognitive Scaffolding.
Claude calls it its memory prosthetic."
How we got here
The enthusiasm phase
ChatGPT, then Gemini, then Mistral. Incredible speed deploying task-specific tools. Equally incredible speed accidentally destroying them. We learned that AI without guardrails is a loaded footgun.
Claude, and the first real problems
Switched to Claude. Immediately better at reasoning, coding, and following instructions — when it remembered them. The context window became our daily nemesis. We started documenting every failure.
From frustration to engineering
Instead of working around Claude's limitations, we started building infrastructure to eliminate them. Skills, project files, startup checklists, filesystem connections. Each fix became permanent.
The architecture emerges
Individual fixes coalesced into a system: task management, context preservation, knowledge packaging, deployment automation. We realized this wasn't just personal tooling — it was a product.
Cognitive Scaffolding ships
A complete infrastructure that deploys to any user's machine. Skills that encode domain expertise. A task creator that gives every conversation instant context. Version-controlled, Git-backed, and reproducible.
What we do now
We configure Claude AI around your team's actual tools, documents, and workflows. Not generic prompts — real infrastructure. Your security rules, your deployment procedures, your client data boundaries, encoded into a system that Claude references automatically.
Every team member gets a workspace where AI arrives pre-loaded with context. Every conversation starts where the last one left off. Every piece of tribal knowledge is captured, versioned, and accessible.
We went through the pain so you don't have to. Hundreds of hours of trial and error, compressed into a deployment that takes less than a week.
We build AI that arrives ready to work — because we spent six months teaching it how.
Six months ago, we didn't know what a context window was.
Today we build systems that work around one.
Our AI journey started the way most do — with enthusiasm, a free ChatGPT account, and no idea what we were getting into. We moved through Google Gemini, got stuck in Mistral, and eventually landed on Claude, which is where things got serious.
And like any serious relationship, it came with serious frustrations.
Claude is brilliant. Claude also forgets everything the moment a conversation ends. It will confidently ignore the rules you spent three hours writing. It will overwrite a working config file with a broken one faster than you can say "did you back that up?"
Every day brought a new flavor of the same problem: AI is powerful but stateless, and stateless tools lose your work.
So we stopped building things with AI and started building things for it. Every time Claude hit a wall — the context window expired, the instructions drifted, the filesystem got clobbered — we engineered a fix and made it permanent.
Hundreds of hours later, we had something we didn't expect: a complete deployment infrastructure that lives on Git, connects to local file systems, and gives Claude the long-term memory and structured workflow it was never designed to have.
"We call it Cognitive Scaffolding.
Claude calls it its memory prosthetic."
How we got here
The enthusiasm phase
ChatGPT, then Gemini, then Mistral. Incredible speed deploying task-specific tools. Equally incredible speed accidentally destroying them. We learned that AI without guardrails is a loaded footgun.
Claude, and the first real problems
Switched to Claude. Immediately better at reasoning, coding, and following instructions — when it remembered them. The context window became our daily nemesis. We started documenting every failure.
From frustration to engineering
Instead of working around Claude's limitations, we started building infrastructure to eliminate them. Skills, project files, startup checklists, filesystem connections. Each fix became permanent.
The architecture emerges
Individual fixes coalesced into a system: task management, context preservation, knowledge packaging, deployment automation. We realized this wasn't just personal tooling — it was a product.
Cognitive Scaffolding ships
A complete infrastructure that deploys to any user's machine. Skills that encode domain expertise. A task creator that gives every conversation instant context. Version-controlled, Git-backed, and reproducible.
What we do now
We configure Claude AI around your team's actual tools, documents, and workflows. Not generic prompts — real infrastructure. Your security rules, your deployment procedures, your client data boundaries, encoded into a system that Claude references automatically.
Every team member gets a workspace where AI arrives pre-loaded with context. Every conversation starts where the last one left off. Every piece of tribal knowledge is captured, versioned, and accessible.
We went through the pain so you don't have to. Hundreds of hours of trial and error, compressed into a deployment that takes less than a week.
We build AI that arrives ready to work — because we spent six months teaching it how.