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Hjarni for Engineering Managers

Decisions, process, ownership, and onboarding context in one shared base. So your team's AI answers from how you actually work, and you stop being the bottleneck.

Free to start. No credit card required.

Product example

Ask about your team's decisions, not a generic playbook

When your decision log, runbooks, and ownership map live in Hjarni, every engineer's Claude or ChatGPT answers from how your team actually operates.

Claude
What did we decide about the on-call rotation?
hjarni: notes-get
Your decision log from the March process review: on-call is a weekly rotation, one primary plus one backup, with a Friday handoff. Whoever is on call skips sprint planning that week.
Who owns the payments service now?
Priya took ownership after the Q2 reorg. The runbook lists her as primary and the platform team as backup.

Claude answers a team-process question using the team's shared notes in Hjarni.

The problem

You are the source of truth, and it does not scale. Why on-call works the way it does. Who owns the payments service now. What the team decided about the migration and why. New engineers ask you. New AI chats know none of it. So the same context lives in your head, a few Slack threads, and one doc nobody can find.

Write the team's context down once, for people and their AI

Put decisions, process, ownership, and runbooks in a shared team space. Connect it to Claude or ChatGPT through MCP, and every engineer's assistant reads the same notes. The reasoning behind how your team works stops being tribal knowledge.

What an engineering manager keeps shared

  • Decisions folder: why you chose X, what you tried and rejected, the tradeoffs behind past calls
  • Process folder: on-call, releases, incident response, review expectations
  • Ownership map: who owns which service, who is backup, escalation paths
  • Onboarding folder: the unwritten context every new hire needs in week one

You stop being the bottleneck

A new engineer connects their own AI and asks how deploys work, why the team moved off Redis, or who owns a service. It answers from your notes instead of interrupting you or a senior engineer. Personal notes stay personal; only what you put in the team space is shared, and per-folder roles decide who can edit what.

Your team's context lives in your head. It doesn't have to.

What engineering managers keep in Hjarni

  • Decision logs: the reasoning behind architecture and process calls, so nobody relitigates them
  • Runbooks and process: on-call, releases, incidents, and reviews the whole team follows
  • Ownership and escalation: who owns what and who to reach when something breaks
  • Onboarding context: the tribal knowledge that usually takes a new hire months to absorb
  • Team AI instructions: folder-level rules every engineer's assistant inherits

A shared brain, not another wiki

A wiki decays because people skip it. This does not, because your team's AI reads it when someone asks, so keeping it current pays off the moment they ask their assistant instead of you. Put architecture, decisions, and runbooks in a shared team space and every engineer's Claude or ChatGPT works from the same source. See Hjarni for Teams, or the step-by-step set up your team guide.

Starter template

Skip the blank page

Paste a template link into Claude or ChatGPT and it builds the folders, tags, and AI instructions for you, so you start by adding notes instead of setting things up.

Common questions

Questions engineering managers actually ask

Isn't this just another wiki nobody reads?

The difference is that your AI reads it. A team wiki decays because people skip it; here every engineer's assistant pulls from the notes when it answers, so keeping decisions and runbooks current pays off the moment someone asks their AI instead of you.

Do 1:1s and sensitive notes have to be shared with the team?

No. Personal notes stay in your personal space; only what you put in the team space is shared. Per-folder roles (viewer, editor, admin) then control who on the team sees or edits each folder.

How is this different from Notion or Confluence?

Those are built for people to read. Hjarni is built so ChatGPT and Claude read it directly through MCP, so answers come from your team's real decisions and runbooks instead of a doc someone first has to find.

How does it help onboarding?

A new engineer connects their own AI and asks it how things work. It answers from your architecture, decisions, and runbooks from their first day, instead of interrupting a senior engineer for the unwritten context.

Start here

Write once. You both remember.

Free to start. No credit card required.

Works with Claude and ChatGPT today.