Ship from your first repo
Wire up agents, PR analytics, and outcome tracking the day you create the repo, not after you hit series A.
For Startups
Ship with AI at full speed while tracking review, rework, and quality from the first repository, before invisible delivery debt becomes a scaling problem.
Start with one repository. Keep your tools, workflow, and code ownership.
What you will see
Connect the repository your team already ships from and get an honest view of sustainable speed without adopting heavyweight engineering process.
Wire up agents, PR analytics, and outcome tracking the day you create the repo, not after you hit series A.
Run Claude Code, Cursor, or Copilot and get a clear record of what each agent generated, changed, and shipped.
See how long ideas take to reach production automatically, no standups or Jira wrangling required.
Built for your operating questions
Wire up agents, PR analytics, and outcome tracking the day you create the repo, not after you hit series A.
Run Claude Code, Cursor, or Copilot and get a clear record of what each agent generated, changed, and shipped.
See how long ideas take to reach production automatically, no standups or Jira wrangling required.
Lightweight PR review signals catch risky AI-generated changes before they become 2am incidents.
Tie every merged feature to whether it actually moved the metric you cared about, not just lines of code.
Track agent token spend against shipped value so your AI bill stays proportional to real progress.
From repo to baseline
Choose one GitHub repository with read-only access. No analytics project, new process, or credit card is required.
Use recent commits and pull requests to see cycle time, quality, rework, and how AI-assisted changes are behaving.
Fix the constraint that is slowing durable delivery now, before it becomes a process or quality problem at the next hiring stage.
Developer velocity
The useful question is not how much code your startup generates. It is how quickly valuable changes reach users and stay shipped. Measure that while the system is still simple enough to improve quickly.
Explore developer velocityStartups win by shipping faster than anyone expects. AI agents make that speed possible from day one, but speed without visibility is how you accumulate silent tech debt and quietly break production. FeatureFactory gives you the measurement layer that keeps velocity honest from your very first commit.
Instead of bolting on analytics after you hit scale, you start with cycle time, deploy frequency, and PR quality already wired in. When an agent opens a pull request, you can see whether the change is trivial or risky before it merges, so your two engineers spend review time where it matters. Our developer velocity tooling turns raw git activity into decisions.
The result is compounding speed: you ship, you measure, you adjust, and every loop is faster than the last. That is what it means to be the feature factory that measures what it ships.
It is easy to feel productive when agents generate thousands of lines a week. But line count is not progress, and a startup cannot afford to confuse the two. FeatureFactory closes the loop by tying each shipped feature back to whether it moved the outcome you actually cared about.
Plan the work with structured product plans and acceptance criteria, build it with your agents, then measure the AI-generated code the same way you measure human work. No double standard, no hand-waving. You get a single, honest view of what is working.
For founders reporting to investors, that honesty is leverage. You can show real engineering throughput and real quality trends instead of anecdotes, backed by DORA metrics that mean something.
Analyze the repository you ship from and establish a durable velocity baseline while the team is still moving fast.
Analyze one repo freeOne repository. No credit card. Read-only access.