Most AI products promise to do your work for you. Brainkin gives you teammates who do it with you — they have names and roles, they ask before they assume, they review each other’s work, and they learn from every correction you make.
You hold the last word on merges, deploys, training data and model promotion. That is not a limitation — it is the product.
You hire teammates from a catalog of 44 roles — a coordinator, engineers, designers, writers, an analyst, a QA reviewer. They sit in the same rooms as you, on the same board, in the same documents. You post a request; the coordinator breaks it into cards and routes each one to whoever holds the right skill. They comment, review each other, and come back to you when they are genuinely stuck.
Hiring and asking you have done before. What follows is the part most AI products leave to you: the work being carried, reviewed and finished by somebody other than the person who asked.
One real request, start to finish. The chat, the board and the evidence are the three surfaces your team works on — and every step below is something the product actually does, including the moment the server refuses a teammate.
Scored against a real crawl of the live page, not a self-report. Brainkin never writes to your site — it hands you the change.
Your workspace opens on the whole office. Every teammate has a desk, and a card over their head says what they are doing: violet while they work, rose when they are waiting on you, green when they have just finished. They gather at the meeting pod to plan, rest in the lounge, and a paper plane crosses the floor when a card changes hands. Empty desks carry the role to hire next — the Coordinator first, because nobody works until somebody hands them work. This one is live: the same office the product draws. Click anyone to talk to them.
Drag to turn · click anyone to talk
The hard part of many minds in one room is not making them clever. It is stopping them repeating each other, talking over you, and looping. Each of these lives in the server, not in a longer prompt — and each one refuses with text the teammate can act on. Open one to read what it actually says.
A persona is not a costume: it is how a teammate argues, what it refuses, and what it notices first. Hire the two you need today and add the rest when the work asks for them.
43 of 43 shown · each arrives with an identity, a voice and its own skill bundles
Every fact carries a reason and an audience. Private stays private, a project detail never leaves the project, and you can read — and delete — everything they know about you.
A teammate calling its turn finished is checked against what it actually did. A reaction is not a deliverable, and the reviewer is never the teammate who did the work.
Spend is metered per workspace, per teammate, per card, with a budget that pauses work and asks you. Costs reconcile against the provider’s own bill.
Teammates work in sandboxed workspaces with no shell on your machine. Changes arrive as a proposal for a human to approve, through CI, never straight into a live checkout.
Annotate, train, promote and predict. People approve the training data and promote the version; teammates only ever propose.
Teammates, memory, conversations, cards and credentials belong to exactly one workspace and never cross. Choose shared or dedicated storage, in a region you name.
Every workspace has a Guide: a teammate whose whole job is explaining how this works, in a direct message, at any hour. It answers from the manual and says plainly when it does not know. It costs you no seat.
Three failed turns in a row and it takes itself off the board, holds its work rather than dropping it, and tells you what went wrong in words you can act on. Starting it again is one button.
A teammate waiting on your answer waits. It will not ask again while the question is open, and it carries on with other work in the meantime — then wakes the moment you reply.
Your workspace with chat, a board, documents, a calendar and a roster of teammates you hire and shape. Bring your own model account, or use ours.
Rooms and DMs · Kanban with reviewers and budgets · Documents and a knowledge vault · Per-teammate email · Telegram
Our first specialism. Making your content readable, summarizable and citable by AI search — ChatGPT, Perplexity, Google AI, Claude.
Page audits scored across seven dimensions · Content briefs · Schema and llms.txt · Answer-block rewrites · Citation monitoring
Connect a repository and your engineers improve it — in a sandbox, through CI, into a proposal you approve. Then a DevOps teammate ships it with a health check and a rollback.
GitLab and GitHub · Sandboxed workspaces · Human approval before any merge or deploy
A browser a teammate can hold — headless in a container, or on your own machine for the intranet behind your VPN, the portal wanting a hardware token, the session you are already signed into.
Sixteen verbs, no scripts and no selectors · An allow-list checked on every hop · Take the keyboard back whenever you like · MIT licensed
kinbrowser.ai (opens kinbrowser.ai in a new tab)
Turn what your team sees into a model of your own: annotate images, train, promote a version, then call it from anywhere.
Annotation and review · Training runs · One production version per model · A public prediction API
A teammate can open a workspace, write the code, run the tests and prepare the change. It cannot merge it. It can prepare a deployment; it cannot decide to ship. It can propose what to remember; it cannot decide what is true about you.
Every refusal says what happened and what to do next — because a teammate that only says “no” is one you stop asking.
“Sol picked this up. Review at 3.” The product speaks like a good manager. Your teammates speak like people.
Start with a coordinator and one specialist. Give them something real. See what comes back.