The problem
Everyone on your team re-explains the same context to their AI. One person has the runbook. Another has the decision log. A third has the onboarding notes. None of it is connected. None of it carries across conversations.
Hjarni gives your team a shared knowledge base. Claude and ChatGPT read from it.
How teams use Hjarni
Every team member's AI works from the same context. No copy-pasting. No repeating what you told someone else's AI last week.
A typical team setup
- Architecture folder — stack overview, service boundaries, database schema, design decisions
- Conventions folder — naming, error handling, test patterns, PR guidelines
- Runbooks folder — deploy steps, rollback procedures, incident playbooks
- Decisions folder — why you chose Postgres, why you moved off Redis, what you tried and rejected
- AI instructions per folder — "When asked about deployments, check the runbook first"
What makes it different
- AI-native, not AI-added. The MCP server is built in, not bolted on. Every folder can have its own AI instructions. Claude and ChatGPT follow them.
- Personal and shared together. Personal notes stay personal. The team knowledge base is shared.
- No bundled AI. Team members bring their own ChatGPT or Claude. No per-message costs. No token limits per seat. No surprises on the bill.
- Simple on purpose. Notes, folders, and a connection to your AI. No databases, kanban boards, or page builders. The constraint is the feature.
When someone new joins
They connect Hjarni to their AI. Claude already knows your conventions and decisions. The context that used to live in someone's head is now written down. Their AI can read it on day one.