Asked by engineers, answered for engineers.

Your code stays in your own GitHub or GitLab. Loopsfinity works on branches with the permissions you grant, opens pull requests, and never pushes to protected branches or deploys on its own. Your code isn't used to train models.

Leaving is as simple as revoking access; everything it produced is already in your repos.

A copilot makes one developer faster inside one file. Loopsfinity executes the workflow around the code: it drafts the PRD, breaks it into tickets, sequences them, writes tests first, implements across services, and delivers review-ready PRs.

Your team keeps using their IDEs; Loopsfinity removes the work between the tickets, not the engineers.

Messy is normal; most post-MVP codebases are. The mapping phase is exactly for this: the Architect agents reconstruct the architecture and conventions from the code itself, not from the docs you never wrote.

Where they find genuine hazards (untested critical paths, tangled dependencies), you get told before anything is planned around them. The demo includes running the mapper on one of your real repos so you can judge the result yourself.

Two gates before anything lands, both yours. Gate one is the plan: before execution you see the PRD, the ticket breakdown, and the execution order: edit anything, or reject it. Gate two is the merge: every ticket arrives as a PR your team reviews like any colleague's work.

Between the gates, the board shows exactly what's running, and you can pause or cancel any ticket mid-flight. The deploy go/no-go is a third decision that also stays human.

Tests first, genuinely. For each ticket the test-writing agents produce the suite, including edge cases derived from your actual data models and API contracts, before implementation starts. The implementation's job is to turn red tests green.

You can open any PR and see the test commits precede the implementation commits.

First, the tests catch it; that's why they're written before the code, and agents iterate against the failing suite. If a ticket genuinely stalls, it never silently merges: a named Loopsfinity engineer investigates and either unblocks it or gives you an honest note about why it needs a human decision.

You'll never find broken agent output on your main branch.

Yes. Milestone planning is one entry point; your existing backlog is the other. Assign any ticket to Loopsfinity: it reads the ticket and the relevant code, shares its plan for the change, and codes only after you approve.

Jira today; if you run Linear or another tracker, mention it in the demo request.

The pipeline is language-agnostic; day to day most of our work is in TypeScript/JavaScript, Python, Go and Java across web apps, APIs and services.

If your stack is unusual, tell us in the demo request; we'll be straight about how well the mapper and test tooling cover it before you commit to anything.

No token metering. You pay a one-time setup, then a flat monthly platform fee: $299 setup / $99 per month for Solo Founder (1 Claude account setup), $999 setup / $299 per month for Team (up to 5 members, 5 Claude account setups).

Inference runs on your own Claude, a subscription or API key we wire up during setup, and is billed by Anthropic to you directly. Internally, Loopsfinity keeps quality and cost under control by scoping each agent to only the context its task needs, instead of stuffing the whole repo into every call.

Each customer runs on a fully separate instance: its own database, its own encryption key, its own credentials. There is no shared state between customers, so isolation holds by construction rather than by a filter that could have a bug.

Your git and model credentials never leave your own network; the control plane coordinates and records, but never holds the keys.