About

Specship is built for the ticket-to-PR work teams keep postponing.

Specship is a private-beta AI coding agent for software teams that already plan work in tickets and review changes in pull requests. The product goal is simple: make implementation work faster without removing specs, tests, reviews, budgets, or policy.

What we build

Direct answer: Specship is a private-beta AI coding agent for ticket-to-PR automation. It is built to turn tickets, screenshots, and bug reports into criteria, tests, code changes, and reviewable pull requests.

Specship turns product requests, bug reports, screenshots, and implementation tickets into acceptance criteria, failing tests, code changes, and review-ready pull requests. It is not positioned as a chat-only coding assistant. The durable output is a normal engineering artifact: a branch, a PR, checks, comments, and review context.

Why it exists

AI coding tools are useful, but teams still need a reliable definition of done. Specship is built around the parts that make agent-generated code reviewable: explicit criteria, tests before implementation, visible policy boundaries, and a human review loop.

How the product is designed to work

1. Start from a ticket

The request stays in the tool where the team already tracks work.

2. Turn intent into a spec

The agent drafts acceptance criteria and asks when the request is ambiguous.

3. Write tests first

Failing tests capture the expected behavior before implementation code is written.

4. Open a PR

The team reviews code, tests, coverage, risk notes, and follow-up commits in Git.

Claim accuracy during beta

We intentionally keep public claims narrow while the product is in private beta. Public pricing is not final. SOC 2 is not claimed as complete. Self-hosted runners and enterprise controls should be treated as rolling out or planned unless a current page explicitly says otherwise.

This matters for buyers and for search quality: the site should be useful, accurate, and specific rather than overpromising capabilities that are still being validated.

Proof library

Real customer case studies should only be published after approval. Until then, these clearly labeled synthetic examples show the level of review evidence Specship is being designed to produce.

CSV export ticketCriteria, tests, implementation notes, and PR review outcome. Visual dashboard tableScreenshot interpretation, constraints, responsive checks, and PR checklist. Test-first bug fixBug report, reproduction test, fix summary, and passing checks.

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