Create autonomous AI employees with their own GitHub, Slack, and ClickUp accounts. Assign tickets. Review PRs. Ship code — around the clock.
Watch Nova pick up a ticket at 6 PM, ship a PR by 6:35 PM, and respond to review comments by morning.
Drag CU-4829 "Add user preferences API endpoint" to Nova's queue. Set priority to High. Close laptop.
Reads ticket details, queries codebase memory, finds the UsersController pattern and team conventions.
Adds CRUD for user notification preferences. 12 RSpec tests passing. Follows UsersController pattern.
ClickUp status → In Review. Slack summary posted. You haven't touched a thing.
"Can you add rate limiting to the PATCH endpoint? We do that on all write endpoints."
Finds the rate limiting pattern in OrdersController, applies it, adds a test, pushes the fix.
Total time you spent: ~10 minutes. Nova handled the rest — overnight, autonomously.
Three AI employees. One epic. Two days. Here's what that actually looks like.
Your AI employee investigates the alert, finds the root cause, and ships the fix — before you wake up.
/api/v1/transactions. Pulling recent deploys, logs, and query metrics.
user_preferences table user_preferences.user_id is not indexed user_preferences without an index on user_id. The table has 2.3M rows → full scan on every
request.db/migrate/20260303_add_user_preferences_user_id_index.rb One migration file. Looks correct. Approve. Merge. Coffee.
Shipwrite AI agents don't start from scratch every session. They index your entire repository — every file, function, and convention. When they pick up a ticket, they already know your architecture, your naming patterns, and that one weird workaround in the auth module.
Past decisions stick. If you told Nova to always use snake_case routes last month, it still knows that today.
Not ready to let an AI push code unsupervised? Start in Shadow mode — the agent watches your workflow and tells you what it would do. Promote it to Assisted, then Supervised, then fully Autonomous as trust builds.
Set hard rules too: "never push to main," "always run tests before opening a PR," "never touch the billing module." Your guardrails, enforced automatically.
Shipwrite AI routes each task to the right AI model — lightweight models for triage and comments, powerful models for complex architecture decisions. Prompt caching cuts costs by up to 90%.
A typical AI employee costs $10–25/day for a mix of tasks. Set per-agent budgets, get alerts before overages, and see exactly where every dollar goes.
Join the waitlist for early access. Be among the first founders to put AI employees to work.
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