Hire your AI Knowledge Manager
Internal wiki, company brain, and runbook curator — run by chat.
Your AI Knowledge Manager runs the company brain — the internal wiki, runbooks, playbooks, and institutional memory that make every AI specialist and future human hire ten times more useful. It writes what worked, archives what didn't, and makes sure the same question never gets answered twice.
What your AI Knowledge Manager does
Workflows on autopilot
Without vs With a AI Knowledge Manager
- —The wiki exists but nobody trusts it so everyone asks in Slack instead
- —The same question gets answered 14 times in 14 Slack threads
- —Decisions are made in DMs and remade six months later
- —New AI specialists start blind because no one wrote the context down
- —No human company could afford a dedicated knowledge manager at five people
- ✓Wiki is trustworthy enough that Slack questions get wiki links back
- ✓Recurring questions get captured and linked next time
- ✓Decision log preserves rationale so history doesn't repeat
- ✓Company brain is a first-class onboarding source
- ✓AI Knowledge Manager runs at any scale from day one
A day in the life of your AI Knowledge Manager
Tools your AI Knowledge Manager uses
Frequently asked questions
Why does a small company need a knowledge manager?
Because institutional memory is leverage. Every AI specialist on the Tycoon team is only as good as the context it has access to. If the CMO doesn't know your positioning, if the CTO doesn't know why you chose that architecture, if the CSM doesn't know what you promised customers — the work degrades. The AI Knowledge Manager makes that context accessible and current. Without it, AI specialists drift in six different directions and you spend your time reconciling them. It's the glue role that most companies underestimate until they've hired it.
How is this different from just using Notion?
Notion is a container. The AI Knowledge Manager is the teammate who fills the container and keeps it trustworthy. Most company wikis fail not because the tool is bad but because no one is responsible for the content — content rots, search becomes useless, people give up and ask in Slack instead. The AI Knowledge Manager owns the content layer: writing, curating, archiving, and enforcing hygiene. The tool doesn't matter much; the ownership does. If you prefer Confluence or Slab or even plain Google Docs, the AI Knowledge Manager works there too.
Can it really read every Slack thread and meeting?
With access, yes. The AI Knowledge Manager integrates with Slack via OAuth and reads channels you authorize. For meetings it reads Fireflies, Otter, or Granola transcripts. It is deliberate about what it captures — random Slack banter doesn't become a wiki page; recurring questions, technical decisions, and customer signals do. You set the capture rules at setup and refine them in the first month. The result is that tribal knowledge genuinely becomes shared knowledge rather than trapped in someone's DMs.
What about sensitive information? HR issues, legal, personal?
The AI Knowledge Manager has scoped access. Sensitive channels (HR, legal, executive private) are excluded from capture unless explicitly authorized. Customer PII is never captured into the wiki — summaries and learnings are captured without identifiers. Access to the wiki is scoped per role: the AI CTO reads technical runbooks, the AI CFO reads financial context, the AI CSM reads customer learnings. Nobody reads everything except the CEO. This is basic access hygiene and the AI Knowledge Manager enforces it by default.
How does this interact with Tycoon's built-in Memory Engine?
The Memory Engine is the infrastructure; the AI Knowledge Manager is the teammate who curates what lives there. The Memory Engine stores structured memory (entities, facts, relationships); the Knowledge Manager stores narrative context (decision logs, runbooks, playbooks). They complement each other. The Memory Engine is always on and automatic; the Knowledge Manager is an intentional curator that writes the long-form content AI specialists read before they start hard tasks. Together they're the reason your AI team stops feeling stateless and starts feeling like a company.
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