Blog
Giving AI tools your company context
Practical guides on getting AI to know your business, so no one pastes the same background into every chat. Want it working for your team? Join the waitlist.
- 4 min read
Connect Claude Desktop to your company knowledge in 10 minutes
A step by step guide to connecting Claude Desktop to your company's facts through MCP, written for people who don't code. Ten minutes, no terminal, no jargon.
- 3 min read
The real cost of re-briefing AI: the math for a 15 person team
Pasting context into AI chats feels free because it comes in 90 second pieces. Added up for a 15 person team, it isn't. The math, with every assumption shown.
- 3 min read
The master prompt document: why it stops scaling at three people
The shared context document is every team's first real AI system, and it works. Then the team grows. A short history of one document, version 1 through 7.
- 3 min read
ChatGPT memory: what it actually remembers, and what it can't
ChatGPT memory saves details about you and recalls past chats. Useful, personal and unpredictable. What it covers, where it stops, and what teams need instead.
- 4 min read
How to write your first 50 company facts
Giving AI context about your company starts with a blank page and a hard question: what does it need to know? A practical way to write the first 50 facts.
- 3 min read
How to stop repeating your company context in every AI chat
Every new AI chat starts from zero, so your team pastes the same background daily. Here is the escalation ladder teams climb, and where it actually ends.
- 3 min read
What is MCP? An explanation for people who don't code
MCP keeps showing up in AI product pages and settings menus. Here is what it actually is, explained without code, and why it matters for how your team works.
- 3 min read
Where Custom GPTs work for team knowledge, and where they break
Custom GPTs are genuinely good at repeated tasks. Teams get in trouble when company knowledge moves into them. Here is the breaking point, from real use.
- 3 min read
Why your team's AI output all sounds slightly different
Four AI drafts from one team, sounding like four different companies. Where the drift comes from, why it compounds in client-facing work, and what stops it.
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